Showing papers on "Neuro-fuzzy published in 1997"

Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

Abstract: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming. It's significant to wait for the representative and beneficial books to read. Every book that is provided in better way and utterance will be expected by many peoples. Even you are a good reader or not, feeling to read this book will always appear when you find it. But, when you feel hard to find it as yours, what to do? Borrow to your friends and don't know when to give back it to her or him.

Fuzzy and neural approaches in engineering

Abstract: From the Publisher: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.

Foundations of neuro-fuzzy systems

Abstract: From the Publisher: Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.

Are artificial neural networks black boxes

Abstract: Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

A new approach to fuzzy modeling

Abstract: This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.

Fuzzy Logic for Business, Finance, and Management

Abstract: This is truly an interdisciplinary book for knowledge workers in business, finance, management and socio-economic sciences based on fuzzy logic. It serves as a guide to and techniques for forecasting, decision making and evaluations in an environment involving uncertainty, vagueness, impression and subjectivity. Traditional modeling techniques, contrary to fuzzy logic, do not capture the nature of complex systems especially when humans are involved. Fuzzy logic uses human experience and judgement to facilitate plausible reasoning in order to reach a conclusion. Emphasis is on applications presented in the 27 case studies including Time Forecasting for Project Management, New Product Pricing, and Control of a Parasit-Pest System.

An Approach to Modelling and Simulation of Uncertain Dynamical Systems

Abstract: Coping with uncertainty in dynamical systems has recently received some attention in artificial intelligence (AI), particularly in the fields of qualitative and model-based reasoning. In this paper, we propose an approach to modelling and simulation of uncertain dynamics which is based on the following ideas: We consider (linguistic) descriptions of uncertain functional relationships characterizing the behavior of some dynamical system. Based on a certain interpretation of such rule-based models, we derive a fuzzy function $\tilde{F}$. It will be shown that all (reasonable) fuzzy functions can be approximated to any degree of accuracy in this way. The function $\tilde{F}$ is then used as the "fuzzy" right hand side of a set of differential equations, which leads us to consider fuzzy initial value problems. We are going to propose an interpretation of such problems. Moreover, several aspects of simulation methods for characterizing the set of all system behaviors compatible with this interpretation will be...

Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications

Abstract: From the Publisher: Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background.Sponsored by:IEEE Neural Networks Council

Soft computing: the convergence of emerging reasoning technologies

Abstract: The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

Fuzzy Logic for Planning and Decision Making

Abstract: 1. Introduction. 2. Basic Concepts of Fuzzy Logic. 3. Stochastic and Fuzzy PERT. 4. Fuzzy SMART. 5. The Additive and the Multiplicative AHP. 6. The ELECTRE Systems. 7. Fuzzy Multi-Objective Optimization. 8. Colour Perception. Subject Index. About the Author.

An ART-based fuzzy adaptive learning control network

Abstract: This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data.

A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling

Abstract: This paper presents different approaches to the problem of fuzzy rules extraction by using fuzzy clustering as the main tool. Within these approaches we describe six methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with a genetic algorithms. These approaches attempt to obtain a first approximation to the fuzzy rules without any assumption about the structure of the data. Because the main objective is to obtain an approximation, the methods we propose must be as simple as possible, but also, they must have a great approximative capacity and in that way we work directly with fuzzy sets induced in the variables input space. The methods are applied to four examples and the errors obtained are specified in the different cases.

Fuzzy Q-learning

Abstract: This paper proposes an adaptation of Watkins' Q-learning (1989, 1992) for fuzzy inference systems where both the actions and the Q-functions are inferred from fuzzy rules. This approach is compared with genetic algorithm on the cart-centering problem, showing its effectiveness.

An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering

Abstract: Derives an interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization. These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning. Derivation of the new algorithms is accomplished by minimizing an average generalized distance between the feature vectors and prototypes using gradient descent. A close relationship between the resulting algorithms and fuzzy c-means is revealed by investigating the functionals involved. It is also shown that the fuzzy c-means and fuzzy learning vector quantization algorithms are related to the proposed algorithms if the learning rate at each iteration is selected to satisfy a certain condition.

Fuzzy model identification: selected approaches

Abstract: General Overview.- Fuzzy Identification from a Grey Box Modeling Point of View.- 1. Introduction.- 2. System Identification.- 3. Fuzzy Modeling Framework.- 4. Fuzzy Identification Based on Prior Knowledge.- 5. Example - Tank Level Modeling.- 6. Practical Aspects.- 7. Conclusions and Future Work.- References.- Clustering Methods.- Constructing Fuzzy Models by Product Space Clustering.- 1. Introduction.- 2. Overview of Fuzzy Models.- 3. Structure Selection for Modeling of Dynamic Systems.- 4. Fuzzy Clustering.- 5. Deriving Takagi-Sugeno Fuzzy Models.- 6. Example: pH Neutralization.- 7. Practical Considerations and Concluding Remarks.- A. The Gustafson-Kessel Algorithm - MATLAB Implementation.- References.- Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform.- 1. Introduction.- 2. The Identification Method.- 3. Example 1.- 4. Example 2.- 5. Summary of the Identification Procedure.- 6. Practical Considerations and Concluding Remarks.- References.- Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering.- 1. Introduction.- 2. The Fuzzy C-Means Algorithm.- 3. Using Hierarchical Clustering to Preprocess Data.- 4. Rapid Prototyping of Approximative Fuzzy Models.- 5. Rapid Prototyping of Descriptive Fuzzy Models.- 6. Examples.- 7. Practical Considerations and Concluding Remarks.- A. Proofs of Propositions.- References.- Neural Networks.- Fuzzy Identification Using Methods of Intelligent Data Analysis.- 1. Introduction.- 2. Neuro-Fuzzy Methods.- 3. Density Estimation.- 4. Fuzzy Clustering.- 5. Conclusion.- A. From Rules to Networks.- B. Learning Rule for RBF Networks.- C.Update Equations for Gaussian Mixtures.- D. Adaptation Algorithm for Fuzzy Clustering.- References.- Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN.- 1. Introduction.- 2. Classification of Fuzzy Models.- 3. Fuzzy Neural Networks.- 4. Identification of Singleton Fuzzy Models.- 5. Simulation Results.- 6. Practical Considerations and Concluding Remarks.- References.- Genetic Algorithms.- Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms.- 1. Introduction.- 2. Evolutionary Algorithms and Genetic Fuzzy Systems.- 3. The Fuzzy Model Identification Problem.- 4. The Genetic Fuzzy Identification Method.- 5. Example.- 6. Practical Considerations and Concluding Remarks.- References.- Optimization of Fuzzy Models by Global Numeric Optimization.- 1. Introduction.- 2. Theoretical Aspects of Fuzzy Models.- 3. The Fuzzy Identification Method.- 4. Simulation Results.- 5. Practical Aspects.- References.- Artificial Intelligence.- Identification of Linguistic Fuzzy Models Based on Learning.- 1. Introduction.- 2. Basic Concepts and Notation.- 3. The Identification Problem.- 4. The Fuzzy Identification Method.- 5. Numeric Examples.- 6. Practical Aspects and Concluding Remarks.- References.

Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives

Abstract: Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al automatic fuzzy tuning and its applications, H. Ishigami et al an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al on-line self-structuring fuzzy inference systems for function approximation, H. Bersini fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang intelligent systems for fraud detection, J. Kingdon genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.

Fuzzy logic in autonomous robotics: behavior coordination

Abstract: Most current architectures for autonomous robots are based on a decomposition of the control problem into small units of control, or behaviors. While this decomposition has a number of advantages, it brings about the problem of having to coordinate the execution of different units in order to obtain a globally coherent behavior. In this paper, we discuss how fuzzy logic can be used, and has been used, to address this problem.

Fuzzy evolutionary computation

Abstract: Editor's Preface. Part 1: Fundamentals. 1.1. Evolutionary Algorithms Z. Michalewicz, et al. 1.2. On the Combination of Fuzzy Logic And Evolutionary Computation: A Short Review and Bibliography O. Cordon, et al. 1.3. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components M.A. Lee, H. Esbensen. Part 2: Methodology and Algorithms. 2.1. GA Algorithms in Intelligent Robots T. Fukuda, et al. 2.2. Development of If-Then Rules with the Use of DNA Coding T. Furuhashi. 2.3. Genetic-Algorithm-Based Approaches to Classification Problems H. Ishibuchi, et al. 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems Through Genetic Algorithms M. Sakawa, T. Shibano. 2.5. Multistage Evolutionary Optimization of Fuzzy Systems - Application to Optimal Fuzzy Control J. Kacprzyk. 2.6. Evolutionary Learning in Neural Fuzzy Control Systems D.A. Linkens, H.O. Nyongesa. 2.7. Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach G. Vukovich, J.X. Lee. 2.8. Evolutionary Based Learning of Fuzzy Controllers L. Magdalena, J.R. Velasco. 2.9. GA-Based Generation of Fuzzy Rules O. Nelles. Part 3: Bibliography. 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic J.T. Alander. Subject Index.

Neural and fuzzy methods in handwriting recognition

Abstract: Handwriting recognition requires tools and techniques that recognize complex character patterns and represent imprecise, common-sense knowledge about the general appearance of characters, words and phrases. Neural networks and fuzzy logic are complementary tools for solving such problems. Neural networks, which are highly nonlinear and highly interconnected for processing imprecise information, can finely approximate complicated decision boundaries. Fuzzy set methods can represent degrees of truth or belonging. Fuzzy logic encodes imprecise knowledge and naturally maintains multiple hypotheses that result from the uncertainty and vagueness inherent in real problems. By combining the complementary strengths of neural and fuzzy approaches into a hybrid system, we can attain an increased recognition capability for solving handwriting recognition problems. This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.