Showing papers on "Neuro-fuzzy published in 2011"
Book•
01 Jan 2011
TL;DR: This book effectively constitutes a detailed annotated bibliography in quasitextbook style of the some thousand contributions deemed by Messrs. Dubois and Prade to belong to the area of fuzzy set theory and its applications or interactions in a wide spectrum of scientific disciplines.
Abstract: (1982). Fuzzy Sets and Systems — Theory and Applications. Journal of the Operational Research Society: Vol. 33, No. 2, pp. 198-198.
5,861 citations
TL;DR: An overview of the proposed interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based systems is presented and a taxonomy based on a double axis is proposed: ''Complexity versus semantic interpretability'' considering the two main kinds of measures.
455 citations
TL;DR: The current contribution constitutes a review on the most representative genetic fuzzy systems relying on Mamdani-type fuzzy rule-based systems to obtain interpretable linguistic fuzzy models with a good accuracy.
322 citations
TL;DR: A hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms and can reveal encouraging results.
Abstract: Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper, a hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.
264 citations
TL;DR: A logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation is proposed, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithsmic nonlinear programming and derives crisp priorities from fuzzy Pairwise comparison matrices.
257 citations
01 Feb 2011
TL;DR: A novel fuzzy expert system can work effectively for diabetes decision support application and the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application.
Abstract: An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.
243 citations
TL;DR: In this paper, the concept of fuzzy soft topology is introduced and some of its structural properties such as neighborhood of a fuzzysoft set, interior fuzzy soft set, fuzzy soft basis, and fuzzy soft subspace topology are studied.
Abstract: In this paper, the concept of fuzzy soft topology is introduced and some of its structural properties such as neighborhood of a fuzzy soft set, interior fuzzy soft set, fuzzy soft basis, fuzzy soft subspace topology are studied.
215 citations
TL;DR: In this article, a hybrid GA-ANFIS model was proposed for building energy prediction, where GA optimizes the subtractive clustering's radiuses to form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance.
197 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
Book•
26 Sep 2011
TL;DR: Fuzzy Modelling Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications and addresses the main design principles governing the development of rule-based models.
Abstract: From the Publisher:
Fuzzy Modelling Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks, and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.
170 citations
TL;DR: A class of evolving fuzzy rule-based system whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning is introduced, suggesting that multivariable Gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling.
Abstract: This paper introduces a class of evolving fuzzy rule-based system as an approach for multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno (eTS) functional fuzzy model, whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning. The fuzzy sets of the rule antecedents are multivariable Gaussian membership functions, which have been adopted to preserve information between input variable interactions. The parameters of the membership functions are estimated by the clustering algorithm. A weighted recursive least-squares algorithm updates the parameters of the rule consequents. Experiments considering time-series forecasting and nonlinear system identification are performed to evaluate the performance of the approach proposed. The multivariable Gaussian evolving fuzzy models are compared with alternative evolving fuzzy models and classic models with fixed structures. The results suggest that multivariable Gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling.
TL;DR: This study integrates kernel functions with fuzzy rough set models and proposes two types of kernelized fuzzy rough sets, and extends the measures existing in classical rough sets to evaluate the approximation quality and approximation abilities of the attributes.
Abstract: Kernel machines and rough sets are two classes of commonly exploited learning techniques. Kernel machines enhance traditional learning algorithms by bringing opportunities to deal with nonlinear classification problems, rough sets introduce a human-focused way to deal with uncertainty in learning problems. Granulation and approximation play a pivotal role in rough sets-based learning and reasoning. However, a way how to effectively generate fuzzy granules from data has not been fully studied so far. In this study, we integrate kernel functions with fuzzy rough set models and propose two types of kernelized fuzzy rough sets. Kernel functions are employed to compute the fuzzy T-equivalence relations between samples, thus generating fuzzy information granules in the approximation space. Subsequently fuzzy granules are used to approximate the classification based on the concepts of fuzzy lower and upper approximations. Based on the models of kernelized fuzzy rough sets, we extend the measures existing in classical rough sets to evaluate the approximation quality and approximation abilities of the attributes. We discuss the relationship between these measures and feature evaluation function ReliefF, and augment the ReliefF algorithm to enhance the robustness of these proposed measures. Finally, we apply these measures to evaluate and select features for classification problems. The experimental results help quantify the performance of the KFRS.
TL;DR: A new hybrid approach is proposed, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal, where significant improvements regarding forecasting accuracy are attainable.
TL;DR: Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient ''If-Then'' rules, which efficiently capture the factor of uncertainty.
TL;DR: A multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed is employed.
Abstract: Surface electromyogram (sEMG) signals, a noninvasive bioelectric signal, can be used for the rehabilitation and control of artificial extremities. Current sEMG pattern-recognition systems suffer from a limited number of patterns that are frequently intensified by the unsuitable accuracy of the instrumentation and analytical system. To solve these problems, we designed a multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed. In this paper, we utilized the sEMG signals to classify and recognize six classes of hand movements. We employed an adaptive neuro-fuzzy inference system (ANFIS) to identify hand motion commands. Training the fuzzy system was performed by a hybrid back propagation and least-mean-square algorithm, and for optimizing the number of fuzzy rules, a subtractive-clustering algorithm was utilized. Furthermore, this paper employed time and time-frequency domains and their combination as the features of the sEMG signal. The proposed recognition scheme utilizing the combined features with an ANFIS classification provided the best result in identifying complex hand movements. The maximum identification accuracy rate of 100% and an average classification accuracy of the proposed ANFIS system of 92% proved to be superior in comparison with relevant studies to date.
TL;DR: Based on the comparisons, it is found that the S-ANFIS model yields plausible accuracy with fewer amounts of computations as compared to the G- ANFIS and MLP models in modeling the ET0 process.
TL;DR: A new method to handle forecasting problems using high-order fuzzy logical relationships and automatic clustering techniques and shows that the proposed method gets a higher average forecasting accuracy rate than the existing methods.
Abstract: Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting enrollments, temperature, and the stock index. If we can get better forecasting accuracy rates, then we can get more benefits. In this paper, we present a new method to handle forecasting problems using high-order fuzzy logical relationships and automatic clustering techniques. The proposed method uses the proposed automatic clustering algorithm to partition the universe of discourse into different lengths of intervals. We also apply the proposed method to forecast the enrollments of the University of Alabama, the temperature and the TAIFEX. The experimental results show that the proposed method gets a higher average forecasting accuracy rate than the existing methods.
Book•
01 Jan 2011
TL;DR: From Interval (Set) and Probabilistic Granules to Set-and-Probabilisticgranules of Higher Order.
Abstract: From Interval (Set) and Probabilistic Granules to Set-and-Probabilistic Granules of Higher Order .- Artificial Intelligence Perspectives on Granular Computing .- Calculi of Approximation Spaces in Intelligent Systems .- Feature Discovery through Hierarchies of Rough Fuzzy Sets .- Comparative Study of Fuzzy Information Processing in Type-2 Fuzzy Systems .- Type-2 Fuzzy Similarity on Partial Truth and Intuitionistic Reasoning .- Decision-Making with Second Order Information Granules .- On the Usefulness of Fuzzy Rule Based Systems based on Hierarchical Linguistic Fuzzy Partitions .- Fuzzy Information Granulation with Multiple Levels of Granularity .- A Rough Set Approach to Building Association Rules and Its Applications .- Fuzzy Modeling with Grey Prediction for Designing Power System Stabilizers .- A Weighted Fuzzy Time Series Based Neural Network Approach to Option Price Forecasting .- A Rough Set Approach to Human Resource Development in IT Corporations .- Environmental Applications of Granular Computing and Intelligent Systems.
TL;DR: This work presents the adaptive neurocomplex-fuzzy-inferential system (ANCFIS), which is the first neurofuzzed system architecture to implement complex fuzzy rules (and, in particular, the signature property of rule interference).
Abstract: Complex fuzzy sets (CFSs) are an extension of type-1 fuzzy sets in which the membership of an object to the set is a value from the unit disc of the complex plane. Although there has been considerable progress made in determining the properties of CFSs and complex fuzzy logic, there has yet to be any practical application of this concept. We present the adaptive neurocomplex-fuzzy-inferential system (ANCFIS), which is the first neurofuzzy system architecture to implement complex fuzzy rules (and, in particular, the signature property of rule interference). We have applied this neurofuzzy system to the domain of time-series forecasting, which is an important machine-learning problem. We find that ANCFIS performs well in one synthetic and five real-world forecasting problems and is also very parsimonious. Experimental comparisons show that ANCFIS is comparable with existing approaches on our five datasets. This work demonstrates the utility of complex fuzzy logic on real-world problems.
TL;DR: A fuzzy min-max neural network based on data core (DCFMN) is proposed for pattern classification and has strong robustness and high accuracy in classification taking onto account the effect of data core and noise.
Abstract: A fuzzy min-max neural network based on data core (DCFMN) is proposed for pattern classification. A new membership function for classifying the neuron of DCFMN is defined in which the noise, the geometric center of the hyperbox, and the data core are considered. Instead of using the contraction process of the FMNN described by Simpson, a kind of overlapped neuron with new membership function based on the data core is proposed and added to neural network to represent the overlapping area of hyperboxes belonging to different classes. Furthermore, some algorithms of online learning and classification are presented according to the structure of DCFMN. DCFMN has strong robustness and high accuracy in classification taking onto account the effect of data core and noise. The performance of DCFMN is checked by some benchmark datasets and compared with some traditional fuzzy neural networks, such as the fuzzy min-max neural network (FMNN), the general FMNN, and the FMNN with compensatory neuron. Finally the pattern classification of a pipeline is evaluated using DCFMN and other classifiers. All the results indicate that the performance of DCFMN is excellent.
TL;DR: The proposed fuzzy ranking method provides a useful way to deal with fuzzy risk analysis problems based on generalized fuzzy numbers and can overcome the drawbacks of some existing methods.
Abstract: In this paper, we present a new method for analyzing fuzzy risk based on a new method for ranking generalized fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers. It considers the areas on the positive side, the areas on the negative side and the heights of the generalized fuzzy numbers to evaluate ranking scores of the generalized fuzzy numbers. The proposed method can overcome the drawbacks of some existing methods for ranking generalized fuzzy numbers. Then, we apply the proposed method for ranking generalized fuzzy numbers to develop a new method for dealing with fuzzy risk analysis problems. The proposed method provides us with a useful way to deal with fuzzy risk analysis problems based on generalized fuzzy numbers.
TL;DR: This study applies fuzzy linear programming to a less emphasized, but important issue in manufacturing, namely that of product mix prioritization and the proposed algorithm provides several advantages to existing algorithm as it carries increased ease in understanding, in use, and provides flexibility in its application.
TL;DR: The equivalence between ELM and the positive and negative fuzzy rule system is revealed, so ELM can be naturally used for training thepositive and negative fuzzier rule system quickly for image classification.
Abstract: We often use the positive fuzzy rules only for image classification in traditional image classification systems, ignoring the useful negative classification information. Thanh Minh Nguyen and QMJonathan Wu introduced the negative fuzzy rules into the image classification, and proposed combination of positive and negative fuzzy rules to form the positive and negative fuzzy rule system, and then applied it to remote sensing image/natural image classification. Their experiments demonstrated that their proposed method has achieved promising results. However, since their method was realized using the feedforward neural network model which requires adjusting the weights in the gradient descent way, the training speed is very slow. Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFNs) learning algorithm, which has distinctive advantages such as quick learning, good generalization performance. In this paper, the equivalence between ELM and the positive and negative fuzzy rule system is revealed, so ELM can be naturally used for training the positive and negative fuzzy rule system quickly for image classification. Our experimental results indicate this claim.
TL;DR: Two hybrid neural networks derived from fuzzy neural networks (FNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN are presented.
Abstract: This paper presents two hybrid neural networks derived from fuzzy neural networks (FNN): wavelet fuzzy neural network (WFNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN. The learning through FNCI is simplified by the use of q-measure and the speed of convergence of the parameters is increased by reinforced learning. The underlying fuzzy models of these hybrid networks are a modified form of fuzzy rules of Takagi-Sugeno model. The number of fuzzy rules is found from a fuzzy curve corresponding to each input-output by counting the total number of peaks and troughs in the curve. The models can forecast hourly load with a lead time of 1 h as they deal with short-term load forecasting. The results of the two hybrid networks using Indian utility data are compared with ANFIS and other conventional methods. The performance of the proposed WFNN is found superior to all the other compared methods.
TL;DR: The experimental results show that the proposed weighted fuzzy interpolative reasoning method by the use of the optimally learned weights that were obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods.
Abstract: In this paper, we propose a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. It is based on a genetic algorithm (GA)-based weight-learning technique. The proposed method can deal with fuzzy rule interpolation with weighted antecedent variables. It also can deal with fuzzy rule interpolation based on polygonal membership functions and bell-shaped membership functions. We also propose a GA-based weight-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules. Furthermore, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, the computer activity prediction problem, multivariate regression problems, and time series prediction problems. Based on statistical analysis techniques, the experimental results show that the proposed weighted fuzzy interpolative reasoning method by the use of the optimally learned weights that were obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods.
TL;DR: This paper makes the first attempt to design a special particle swarm algorithm to determine a type of general fuzzy measures from data, and demonstrates that the algorithm is effective and efficient and is extended to identify and revise other types of fuzzy measures.
01 Jun 2011
TL;DR: In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type- 1 fuzzy systems can be obtained appropriately.
Abstract: A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.
TL;DR: This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis, and detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine.
Abstract: In this paper, a survey has been made on the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification. Several state-of-the-art Artificial Intelligence (AI) techniques for automation of biomedical image classification are investigated. This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis. It also detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine. SVM neural network is used in almost all imaging modalities of medical image classification. Similarly fuzzy C means and improvements to it are important tool in segmentation of brain images. Various diagnostic studies like mammogram analysis, MRI brain analysis, bone and retinal analysis etc., using neural network approach result in use of back propagation network, probabilistic neural network, and extreme learning machine recurrently. Hybrid approach of GA and PSO are also commonly used for feature extraction and feature selection.
TL;DR: The method uses a fuzzy relation to describe the relation between vertices as well as the similarity in network topology to determine the membership grade of the relation and transforms this fuzzy relation into a fuzzy equivalence relation.
TL;DR: The fuzzy neural network enables us to define bands that are expected to contain given percentages of forecasted level/discharge values for each lead time selected; an analysis of the results obtained reveals that these bands generally have a slightly smaller width compared to the bands obtained using other data-driven models.
Abstract: This paper proposes a new procedure for water level (or discharge) forecasting under uncertainty using artificial neural networks: uncertainty is expressed in the form of a fuzzy number. For this purpose, the parameters of the neural network, namely, the weights and biases, are represented by fuzzy numbers rather than crisp numbers. Through the application of the extension principle (Zadeh, 1965), the fuzzy number representative of the output variable is then calculated at each time step on the basis of a set of crisp inputs and fuzzy parameters of the neural network.The fuzzy parameters of the neural network are estimated from the modelling process, that is through a calibration procedure that imposes a constraint whereby for an assigned h-level the envelope of the corresponding intervals representing the outputs (forecasted levels or discharges, calculated at different points in time) must include a preset percentage of observed values.The application of the model to two specific cases and a comparison of the results with those provided by other data-driven models - Bayesian neural networks (Neal, 1992) and the Local Uncertainty Estimation Model (Shrestha and Solomatine, 2006) - show its effectiveness in estimating water levels or discharges under uncertainty. The fuzzy neural network enables us to define bands that are expected to contain given percentages of forecasted level/discharge values for each lead time selected; an analysis of the results obtained reveals that these bands (e.g., 99%, 95% and 90% uncertainty bands) generally have a slightly smaller width compared to the bands obtained using other data-driven models.