Showing papers on "Soft computing published in 2010"

Review: The use of computational intelligence in intrusion detection systems: A review

Abstract: Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

Soft sets combined with fuzzy sets and rough sets: a tentative approach

Abstract: Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Dubois and Prade investigated the problem of combining fuzzy sets with rough sets. Soft set theory was proposed by Molodtsov as a general framework for reasoning about vague concepts. The present paper is devoted to a possible fusion of these distinct but closely related soft computing approaches. Based on a Pawlak approximation space, the approximation of a soft set is proposed to obtain a hybrid model called rough soft sets. Alternatively, a soft set instead of an equivalence relation can be used to granulate the universe. This leads to a deviation of Pawlak approximation space called a soft approximation space, in which soft rough approximations and soft rough sets can be introduced accordingly. Furthermore, we also consider approximation of a fuzzy set in a soft approximation space, and initiate a concept called soft---rough fuzzy sets, which extends Dubois and Prade's rough fuzzy sets. Further research will be needed to establish whether the notions put forth in this paper may lead to a fruitful theory.

Fuzzy set theory

Abstract: Since its inception in 1965, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found, for example, in artificial intelligence, computer science, medicine, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, and robotics. Mathematical developments have advanced to a very high standard and are still forthcoming to day. In this review, the basic mathematical framework of fuzzy set theory will be described, as well as the most important applications of this theory to other theories and techniques. Since 1992 fuzzy set theory, the theory of neural nets and the area of evolutionary programming have become known under the name of ‘computational intelligence’ or ‘soft computing’. The relationship between these areas has naturally become particularly close. In this review, however, we will focus primarily on fuzzy set theory. Applications of fuzzy set theory to real problems are abound. Some references will be given. To describe even a part of them would certainly exceed the scope of this review. Copyright © 2010 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website.

Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

Abstract: The conventional digital hardware computational blocks with different structures are designed to compute the precise results of the assigned calculations. The main contribution of our proposed Bio-inspired Imprecise Computational blocks (BICs) is that they are designed to provide an applicable estimation of the result instead of its precise value at a lower cost. These novel structures are more efficient in terms of area, speed, and power consumption with respect to their precise rivals. Complete descriptions of sample BIC adder and multiplier structures as well as their error behaviors and synthesis results are introduced in this paper. It is then shown that these BIC structures can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

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.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

Abstract: Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

Abstract: This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

Review article: A review of soft computing applications in supply chain management

Abstract: It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers' attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research.

Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques

Abstract: The performance of a solar collector system using sodium carbonate decahydrate (Na"2CO"3.10H"2O) as Phase Change Material (PCM) was experimentally investigated during March and collector efficiency was compared with those of convectional system including no PCM. We also made a series of predictions by using three different soft computing techniques as Artificial Neural Networks (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). It was found that the solar collector system with PCM is more effective than convectional systems. Soft computing techniques can be used to model of a solar collector with PCM. Furthermore, analysis of soft computing showed that SVM technique gives the best results than that of ANFIS and ANN.

Comparison between prediction capabilities of neural network and fuzzy logic techniques for L and slide susceptibility mapping.

Abstract: Preparation of L and slide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of L and slides, producing a reliable susceptibility map is not easy. In recent years, various data mining and soft computing techniques are getting popular for the prediction and classification of L and slide susceptibility and hazard mapping. This paper presents a comparative analysis of the prediction capabilities between the neural network and fuzzy logic model for L and slide susceptibility mapping in a geographic information system (GIS) environment. In the first stage, L and slide-related factors such as altitude, slope angle, slope aspect, distance to drainage, distance to road, lithology and normalized difference vegetation index (ndvi) were extracted from topographic and geology and soil maps. Secondly, L and slide locations were identified from the interpretation of aerial photographs, high resolution satellite imageries and extensive field surveys. Then L and slide-susceptibility maps were produced by the application of neural network and fuzzy logic approahc using the aforementioned L and slide related factors. Finally, the results of the analyses were verified using the L and slide location data and compared with the neural network and fuzzy logic models. The validation results showed that the neural network model (accuracy is 88%) is better in prediction than fuzzy logic (accuracy is 84%) models. Results show that "gamma" operator (X = 0.9) showed the best accuracy (84%) while "or" operator showed the worst accuracy (66%).

A soft computing method for detecting lifetime building thermal insulation failures

Abstract: The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between input and output variables and data dimensionality reduction. It also applies system identification theory and neural networks for modeling the thermal dynamics of the building. Finally, the novel model is used to predict dynamic thermal biases, and two real cases of study as part of its empirical validation.

Analysis of seasonal time series using fuzzy approach

Abstract: A new methodology for the analysis and forecasting of time series is proposed. It directly employs two soft computing techniques: the fuzzy transform and the perception-based logical deduction. Thanks to the use of both these methods, and to the innovative approach, consisting of the construction of several independent models, the methodology is successfully applicable to robust long-time predictions.

Credit Scoring Models using Soft Computing Methods: A Survey

Abstract: During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main modelling issues are discussed especially from the data mining point of view. The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.

Kansei Engineering and Soft Computing: Theory and Practice

Abstract: Kansei Engineering and Soft Computing: Theory and Practice offers readers a comprehensive review of kansei engineering, soft computing techniques, and the fusion of these two fields from a variety of viewpoints. It explores traditional technologies, as well as solutions to real-world problems through the concept of kansei and the effective utilization of soft computing techniques. This publication is an essential read for professionals, researchers, and students in the field of kansei information processing and soft computing providing both theoretical and practical viewpoints of research in humanized technology.

A comparative study of PID controller tuning using GA, EP, PSO and ACO

Abstract: The aim of this paper is to study the tuning of a PID controller using soft computing techniques The methodology and efficiency of the proposed method are compared with that of traditional methods Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability

Real Life Applications of Soft Computing

Abstract: Rapid advancements in the application of soft computing tools and techniques have proven valuable in the development of highly scalable systems and resulted in brilliant applications, including those in biometric identification, interactive voice response systems, and data mining. Although many resources on the subject adequately cover the theoreti

On concept algebra for computing with words (cww)

Abstract: Computing with words (CWW) is an intelligent computing methodology for processing words, linguistic variables, and their semantics, which mimics the natural-language-based reasoning mechanisms of human beings in soft computing, semantic computing, and cognitive computing. The central objects in CWW techniques are words and linguistic variables, which may be formally modeled by abstract concepts that are a basic cognitive unit to identify and model a concrete entity in the real world and an abstract object in the perceived world. Therefore, concepts are the most fundamental linguistic entities that carries certain meanings in expression, thinking, reasoning, and system modeling, which may be formally modeled as an abstract and dynamic mathematical structure in denotational mathematics. This paper presents a formal theory for concept and knowledge manipulations in CWW known as concept algebra. The mathematical models of abstract and concrete concepts are developed based on the object-attribute-relation (OAR) theory. The formal methodology for manipulating knowledge as a concept network is described. Case studies demonstrate that concept algebra provides a generic and formal knowledge manipulation means, which is capable of dealing with complex knowledge and their algebraic operations in CWW.

Neural Network-A Novel Technique for Software Effort Estimation

Abstract: Estimating software development effort is an important task in the management of large software projects. The task is challenging and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel model to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to utilize improved estimation of software effort for NASA software projects. A comparison between Artificial-Neural-Network Based Model (ANN) and Halstead, Walston-Felix, Bailey-Basili and Doty models were provided. The evaluation criteria are based upon MRE and MMRE. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. .The results show that ANNs are effective in effort estimation.

Soft Computing Approaches for Prediction of Software Maintenance Effort

Abstract: The relationship between object oriented metrics and software maintenance effort is complex and non-linear. Therefore, there is considerable research interest in development and application of sophisticated techniques which can be used to construct models for predicting software maintenance effort. The aim of this paper is to evaluate and compare the application of different soft computing techniques – Artificial Neural Networks, Fuzzy Inference Systems and Adaptive Neuro-Fuzzy Inference Systems to construct models for prediction of Software Maintenance Effort. The maintenance effort data of two commercial software products is used in this study. The dependent variable in our study is maintenance effort. The independent variables are eight Object Oriented metrics . It is observed that soft computing techniques can be used for constructing accurate models for prediction of software maintenance effort and Adaptive Neuro Fuzzy Inference System technique gives the most accurate model.

Combining Soft Computing and Statistical Methods in Data Analysis

Abstract: Thank you for downloading combining soft computing and statistical methods in data analysis. As you may know, people have look hundreds times for their chosen books like this combining soft computing and statistical methods in data analysis, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their desktop computer.

Opposition based computing — A survey

Abstract: In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications are from different fields, such as optimization algorithms, learning algorithms and fuzzy logic. The reported results confirm that OBL paradigm was promising to accelerate or to enhance accuracy of soft computing algorithms. In this paper, a survey of existing applications of opposition-based computing is presented.

What is Soft Computing? Revisiting Possible Answers

Abstract: The term Soft Computing was coined by L.A. Zadeh in the early 90's. Since that time many researchers have tried to define it considering different approaches: main constituents, properties, abilities, etc. In addition, the term Computational Intelligence has also gained popularity having a somehow quite close meaning to that of Soft Computing. The central idea of this paper is to present, analyze, compare and discuss a few of the definitions that can be found on literature; not trying to find the best but to offer the reader arguments to make his/her own decision.

Interval-valued Fuzzy Sets in Soft Computing

Abstract: In this work, we explain the reasons for which, for some specific problems, interval valued fuzzy sets must be considered a basic component of Soft Computing

Soft Computing for Reservoir Characterization and Modeling

Abstract: The volume is the first comprehensive book in the area of intelligent reservoir characterization written by leading experts in academia and industry. It contains state-of-the-art techniques to be applied in reservoir geophysics, well logging, reservoir geology, and reservoir engineering. It introduces the basic concepts of soft computing techniques including neural networks, fuzzy logic and evolutionary computing applied to reservoir characterization. Some advanced statistical and hybrid models are also presented. The specific applications include different reservoir characterization topics such as prediction of petrophysical properties from well logs and seismic attributes.

A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

Abstract: Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.