Showing papers on "Soft computing published in 2011"
Proceedings Article•
01 Jan 2011
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Abstract: (Knowledge Extraction based onEvolutionary Learning) tool, an open source software that supports datamanagement and a designer of experiments. KEEL pays special attentionto the implementation of evolutionary learning and soft computing basedtechniques for Data Mining problems including regression, classification,clustering, pattern mining and so on.The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformatandshowssomeresultsofalgorithmsinthesedatasets; someguidelines for including new algorithms in KEEL, helping the researcherstomaketheirmethodseasilyaccessibletootherauthorsandtocomparetheresults of many approaches already included within the KEEL software;and a module of statistical procedures developed in order to provide to theresearcher a suitable tool to contrast the results obtained in any experimen-talstudy.Acaseofstudyisgiventoillustrateacompletecaseofapplicationwithin this experimental analysis framework.
2,057 citations
TL;DR: In this article, the use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared.
Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.
363 citations
TL;DR: The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research.
257 citations
01 Jan 2011
TL;DR: The CD contains the following content: power point presentations, source codes for Soft Computing Techniques in C, MATLAB Source code programs, and program files as per their problem numbers in their respective chapters.
Abstract: The CD contains the following content.
1. Power point presentations
* Presentations are given for Chapters 1–17, 19.
* MATLAB Soft Computing tools presentations are also included for easy reference of the readers to know the basic commands.
2. Source Codes for Soft Computing Techniques in C
* Source codes are given for all the problems solved in Chapter 18.
* The programs are as *.txt files.
3. MATLAB Source code programs
* MATLAB Source codes are given for problems solved in Chapter 19.
* The program files are given as per their problem numbers in their respective chapters.
4. Copyright page
Do install the required software before running the programs given.
198 citations
01 Jan 2011
TL;DR: A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper and successful results are obtained.
Abstract: Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, @s parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained.
192 citations
01 Jan 2011
TL;DR: A new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods is presented, presented to illustrate the application of the proposed framework and its functioning.
Abstract: In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.
190 citations
01 Jan 2011
TL;DR: Two evolutionary computing approaches namely differential evolution and opposition based differential evolution combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification.
Abstract: This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance.
157 citations
01 Mar 2011
TL;DR: Experimental results on FSVM illustrate that it is better capable of extracting useful information from corporate data and a comparative study of clustering power of FSVM is made with PNN on ripley and bankruptcy datasets, showing that FSVM has superior clusteringPower than PNN.
Abstract: Bankruptcy prediction has been a topic of active research for business and corporate organizations since past few decades. The problem has been tackled using various models viz., Statistical, Market Based and Computational Intelligence in the past. Among Computational Intelligence models, Artificial Neural Network has become dominant modeling paradigm. In this Paper, we use a novel Soft Computing tool viz., Fuzzy Support Vector Machine (FSVM) to solve bankruptcy prediction problem. Support Vector Machine is a powerful statistical classification technique based on the idea of Structural Risk Minimization. Fuzzy Sets are capable of handling uncertainty and impreciseness in corporate data. Thus, using the advantage of Machine Learning and Fuzzy Sets prediction accuracy of whole model is enhanced. FSVM is implemented for analyzing predictors as financial ratios. A method of adapting it to default probability estimation is proposed. The test dataset comprises of 50 largest bankrupt organizations with capitalization of no less than $1 billion that filed for protection against creditors under Chapter 11 of United States Bankruptcy Code in 2001-2002 after stock marked crash of 2000. Experimental results on FSVM illustrate that it is better capable of extracting useful information from corporate data. This is followed by a comparative study of FSVM with other approaches. FSVM is effective in finding optimal feature subset and parameters. This is evident from the results thus improving prediction of bankruptcy. The choice of feature subset has positive influence on appropriate kernel parameters and vice versa which demonstrate its appreciable generalization performance than traditional bankruptcy prediction methods. Choosing appropriate value of parameter plays an important role on the performance of FSVM model. The effect of variability in prediction performance of FSVM with respect to various values of different parameters of SVM is also investigated. Finally, a comparative study of clustering power of FSVM is made with PNN on ripley and bankruptcy datasets. The results show that FSVM has superior clustering power than PNN.
149 citations
TL;DR: Several soft computing-based models, namely Support Vector Machines, Bayesian Networks, artificial Neural Networks, Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System are used for mapping wind data to wave height.
132 citations
Book•
02 Sep 2011
TL;DR: The first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics is as mentioned in this paper, which can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioINformatics and geoscience.
Abstract: This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.
123 citations
01 Jun 2011
TL;DR: This work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps, and reveals its comparative advantage over the benchmarking machine learning algorithms tested for the same data set for the years mentioned.
Abstract: This work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps. Fuzzy cognitive map (FCM) is a fusion of fuzzy logic and cognitive map theories, and is used for modeling and representing experts' knowledge. It is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. It is a challenging approach for decision making especially in complex processing environments. The FCM approach presented here was chosen to be utilized in agriculture because of the nature of the application. The prediction of yield in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. Throughout this proposed method, FCMs designed and developed to represent experts' knowledge for cotton (Gossypium hirsutum L.) yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main factors affecting cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton yield. The investigated methodology was evaluated for 360 cases measured during the time of six subsequent years (2001-2006) in a 5ha experimental cotton field, in predicting the yield class between two possible categories (''low'' and ''high''). The results obtained reveal its comparative advantage over the benchmarking machine learning algorithms tested for the same data set for the years mentioned by providing decisions that match better with the real measured ones. The main advantage of this approach is its simple structure and flexibility, representing knowledge visually and more descriptively. Hence, it might be a convenient tool in predicting cotton yield and improving crop management.
01 Nov 2011
TL;DR: This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework that combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results.
Abstract: Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue.
TL;DR: In this article, the authors discuss work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc.
Abstract: Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working on different application to make interactions more easy, natural and convenient without wearing any extra device. HGR can be applied from games control to vision enabled robot control, from virtual reality to smart home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc. The methods in the preprocessing of image for segmentation and hand image construction also taken into study. Most researchers used fingertips for hand detection in appearance based modeling. Finally the comparison of results given by different researchers is also presented.
TL;DR: Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem and the investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.
Abstract: The soft computing technique of fuzzy cognitive maps (FCM) for modeling and predicting autistic spectrum disorder has been proposed. The FCM models the behavior of a complex system and is used to develop new knowledge based system applications. FCM combines the robust properties of fuzzy logic and neural networks. To overwhelm the limitations and to improve the efficiency of FCM, a good learning method of unsupervised training could be applied. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is proposed here. Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem. The investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.
Journal Article•
TL;DR: In this article, a generalization of Molodtsov's soft set, called soft multiset, is introduced, with its basic operations such as complement, union and intersection.
Abstract: In 1999 Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. The solutions of such problems involve the use of mathematical principles based on uncertainty and imprecision. In this paper we recall the definition of a soft set, its properties and its operations. As a generalization of Molodtsov’s soft set we introduce the definitions of a soft multiset, its basic operations such as complement, union and intersection. We give examples for these concepts. Basic properties of the operations are also given.
12 Jul 2011
TL;DR: IACOR-LS as discussed by the authors is a variant of ACOR that uses local search and features a growing solution archive, which is a significant improvement over ACOR, but it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal.
Abstract: ACOR is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems.
TL;DR: In this paper, a Soft Computing (SC) based framework for the fragility assessment of 3D buildings is proposed in which a Neural Network (NN) implementation is presented, which can provide accurate predictions of the structural response at a fraction of computational time required by a conventional analysis.
01 Jan 2011
TL;DR: This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle through iterative genetic algorithms an appropriated fuzzy controller.
Abstract: It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmanned control of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative genetic algorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.
01 Jun 2011
TL;DR: This work shows the advantage of ANN over ANFIS and FIS for modeling IEC, and finds that of the three models, ANN model gives the most accurate results using the training algorithm Levenberg-Marquardt (LM).
Abstract: Designing an optimal air conditioning system needs the knowledge of its performance. Soft computing tools like fuzzy inference system (FIS), artificial neural networks (ANN) and adaptive neuro fuzzy inference (ANFIS) provides simple but powerful way for predicting the performance of an IEC. In this paper both analytical as well as soft computing approach is used in predicting the performance of an IEC. All the models are trained with simulation data and are then compared and validated using experimental data from the literature. It was found that of the three models, ANN model gives the most accurate results using the training algorithm Levenberg-Marquardt (LM). The statistical values i.e. R^2, RMS, cov, MSE and AIC using ANN for the prediction of primary air outlet temperature were 0.9999, 0.1830, 0.7811, 0.0335 and -3.38, and for effectiveness were 0.9999, 0.00335, 0.5212, 1.119E-05 and -11.38 respectively. This work shows the advantage of ANN over ANFIS and FIS for modeling IEC.
02 Jan 2011
TL;DR: This paper is discussing work done in the area of hand gesture recognition where focus is on the soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms, etc and comparing results given by different researchers after their implementation.
Abstract: Hand gestures recognition is the natural way of Human Machine interaction and today many researchers in the academia and industry are interested in this direction. It enables human being to interact with machine very easily and conveniently without wearing any extra device. It can be applied from sign language recognition to robot control and from virtual reality to intelligent home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms, etc. We also described hand detection methods in the preprocessed image for detecting the hand image. Most researchers used fingertips for hand detection in appearance based modeling. Finally we are comparing results given by different researchers after their implementation.
Book•
20 Dec 2011TL;DR: Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice, and is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
Abstract: Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as:Soft computing in pattern recognition and data miningA Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the setSelection of non-redundant and relevant features of real-valued data setsSelection of the minimum set of basis strings with maximum information for amino acid sequence analysisSegmentation of brain MR images for visualization of human tissuesNumerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This textcovering the latest findings as well as directions for future researchis recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
Book•
08 Nov 2011
TL;DR: This self-consistent monograph puts together topics coming from mathematical programming, with soft computing and Proper Orthogonal Decomposition, in order to show, in the context of structural analyses, how the things work and what are the main problems one needs to tackle.
Abstract: In this self-consistent monograph, the author gathers and describes different mathematical techniques and combines all together to form practical procedures for the inverse analyses. It puts together topics coming from mathematical programming, with soft computing and Proper Orthogonal Decomposition, in order to show, in the context of structural analyses, how the things work and what are the main problems one needs to tackle. Throughout the book a number of examples and exercises are worked out in order to make reader practically familiar with discussed topics.
TL;DR: The role of soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties is surveyed.
Abstract: The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.
TL;DR: In this paper, an accurate model of the inverted pendulum system, a neural network controller and ANFIS (Adaptive Neuro-Fuzzy Inference System) controller to stabilize the system have been developed.
Abstract: The inverted pendulum is a highly nonlinear and open-loop unstable system. This means that standard linear techniques cannot model the nonlinear dynamics of the system, Inverted pendulum system is often used as a benchmark for verifying the performance and effectiveness of a new control method because of the simplicities of the structure. In this paper an accurate model of the inverted pendulum system, a neural network controller and ANFIS (Adaptive Neuro-Fuzzy Inference System) controller to stabilize the system have been developed. A control law that removes some of the nonlinearities from the process and allows the process to exhibit its dynamics has been developed. This aids in stabilizing the nonlinear pendulum. The quality of the data input has also been improved, since only limited number of variables that can be measured accurately are included in the system identification Simulation results establishes that the proposed controller has good set point tracking and disturbance rejection properties.
01 Dec 2011
TL;DR: Two novel wrapper based hybrid soft computing techniques are proposed in this paper for feature selection and parameters optimization to classify nine types of power disturbances without degrading the SVM classification accuracy.
Abstract: Recognition of the presence of any power disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. In spite of the extensive number of power disturbances classification methods, a research on the selection of useful features from the existing feature set and the parameter optimization for specific classifiers was omitted. The kernel parameters setting for support vector machine (SVM) classifier in training process along with feature selection will significantly impact the classification accuracy. Two novel wrapper based hybrid soft computing techniques are proposed in this paper for feature selection and parameters optimization to classify nine types of power disturbances without degrading the SVM classification accuracy. The feature items were selected from discrete wavelet transform across several decomposition levels of the disturbance signals and from the duration of disturbance occurrence. This analysis selects the more useful feature set and optimized parameters for two types of kernels namely the polynomial kernel and radial basis function kernel for SVM. Compared with the traditional grid algorithm the proposed genetic algorithm and simulated annealing based approach significantly improves the classification accuracy rate by eliminating relatively useless feature items and proper parameter selection for the classifier.
Journal Article•
TL;DR: The aim of this book is to provide a Discussion of the Foundations of Model Hosts and their Applications in Retinal Degenerative Diseases.
Abstract: LaVail (Eds), Retinal Degenerative Diseases (Advances in Experimental Medicine and Biology 723) ISBN 978-1-4614-0630-3 7 * € (D) 213,95 | € (A) 219,94 | sFr 266,50 7 € 199,95 | £180.00 Special_SpacerSpecial_Spacer Mylonakis (Eds), Recent Advances on Model Hosts (Advances in Experimental Medicine and Biology 710) ISBN 978-1-4419-5637-8 7 * € (D) 149,75 | € (A) 153,94 | sFr 201,00 7 € 139,95 | £126.00 Special_SpacerSpecial_Spacer
Book•
21 Jan 2011
TL;DR: In this book, a new systematization of the problems of multiple criteria decision making is proposed which allows the author to reveal unsolved problems and a new approach to building effective stock trading systems, based on the synthesis of fuzzy logic and the Dempster-Shafer theory is developed.
Abstract: Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and logistic.An essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria ones. In this book, a new systematization of the problems of multiple criteria decision making is proposed which allows the author to reveal unsolved problems. The solutions of them are presented as well and implemented to deal with some important real-world problems such as investment projects evaluation, tool steel material selection problem, stock screening and fuzzy logistic. It is well known that the best results in real -world applications can be obtained using the synthesis of modern methods of soft computing. Therefore, the developed by the author new approach to building effective stock trading systems, based on the synthesis of fuzzy logic and the Dempster-Shafer theory, seems to be a considerable contribution to the application of soft computing method in economics and finance.An important problem of capital budgeting is the fuzzy evaluation of the Internal Rate of Return. In this book, this problem is solved using a new method which makes it possible to solve linear and nonlinear interval and fuzzy equations and systems of them. The developed new method allows the author to obtain an effective solution of the Leontjevs input-output problem in the interval setting.
01 Dec 2011
TL;DR: This survey has shown that on an average the artificial neural networks and Bayesian approaches have emerged more successful in EEG analysis than the other soft computing paradigms.
Abstract: Presently high density EEG systems are available at affordable cost, with which the data dimension has gone up considerably. For efficient computation of this high-dimensional data, various soft computing paradigms are receiving increasing attention. In this survey we have identified certain soft computing techniques (by soft computing techniques we mean computational techniques that take into account the inherent uncertainties in the data and/or in the computing model) for pattern recognition/data mining, such as, neural networks, fuzzy logic, evolutionary computation, statistical discrimination and Bayesian inference, which have turned out to be particularly useful in processing human scalp EEG. Wherever possible results of comparative studies among various techniques have been presented. Analyses of EEG for various feature extraction are exceedingly challenging pattern recognition tasks. This survey has shown that on an average the artificial neural networks and Bayesian approaches have emerged more successful in EEG analysis than the other soft computing paradigms. For readability the paper has been kept as little technical as possible. Large number of references have been listed to aid searching for the technical details.
TL;DR: The purpose of this article is to demonstrate the use of feedforward neural networks, adaptive neural fuzzy inference systems, and probabilistic neural networks to discriminate between earthquakes and quarry blasts in Istanbul and vicinity (the Marmara region).
TL;DR: This paper suggests incorporating a new logic: Neutrosophic logic in medical domain and also discusses the possibility of extending the capabilities of the fuzzy systems by employing neutrosophics systems.
Abstract: Soft computing is an enriching domain that helps to encode uncertainty and imprecision that exists in real world. Integration of soft computing techniques in the systems lends added advantage to the existing systems to allow solutions to otherwise unsolvable problems. Fuzzy architecture has been extensively researched and applied in medical domain. This paper suggests incorporating a new logic: Neutrosophic logic in medical domain and also discusses the possibility of extending the capabilities of the fuzzy systems by employing neutrosophic systems. General Terms Medical AI