Showing papers on "Neuro-fuzzy published in 1999"

Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems

Abstract: We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule Thus, our method can be viewed as a classifier system In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained The fixed membership functions also lead to a simple implementation of our method as a computer program The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method The performance of our method is evaluated by computer simulations on some well-known test problems While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks

Case-based reasoning is a methodology not a technology

Abstract: This paper asks whether case-based reasoning is an artificial intelligence (AI) technology like rule-based reasoning, neural networks or genetic algorithms or whether it is better described as a methodology for problem solving, that may use any appropriate technology. By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. The implications of this are discussed.

Computing with Words in Information/Intelligent Systems 1

Abstract: M Sugeno: Foreword- Neuro-Fuzzy and Genetic Systems for Computing with Words: S Mitaim, B Kosko: Neural Fuzzy Intelligent Agents S Siekmann, R Neuneier, H-G Zimmermann, R Kruse: Neuro Fuzzy Systems for Data Analysis J Leski, E Czogala: A New Fuzzy Interference System Based on Artificial Neural Network and its Applications O Cordon, A Gonzales, F Herrera, R Perez: Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling- Tools for Linguistic Data Modeling and Analysis: H Lee, H Tanaka: Fuzzy Graphs with Linguistic Input-Outputs by Fuzzy Approximation Models MA Gil, PA Gil, DA Ralescu: Fuzzy Random Variables: Modeling Linguistic Statistical Data- Linguistic Models in System Reliability, Quality Control and Risk Analysis: T Onisawa, A Ohmori: Linguistic Model of System Reliability Analysis P Grzegorzewski, O Hryniewicz: Lifetime Tests for Vague Data C Huang, D Ruan: Systems Analytic Models for Fuzzy Risk Estimation- Linguistic Models in Decision Making, Optimization and Control: H Kiendl: Decision Analysis by Advanced Fuzzy Systems J kacprzyk, H Nurmi, M Fedrizzi: Group Decision Making and a Measure of Consensus under Fuzzy Preferences and a Fuzzy Linguistic Majority S Chanas, D Kuchta: Linear Programming with Words JJ Buckley, T Feuring: Computing with Words in Control R Kowalczyk: On Linguistic Fuzzy Constraint Satisfaction Problems- Linguistic and Imprecise Information in Databases and Information Systems: G Chen: Data Models for Dealing with Linguistic and Imprecise Information FE Petry, M Cobb, A Morris: Fuzzy Set Approaches to Model Uncertainty in Spatial Data and Geographic Information Systems JC Cubero, JM Medina, OPons, MA Vila: Computing Fuzzy Dependencies with Linguistic Labels J Kacprzyk, S Zadrozny: The Paradigm of Computing with Words in Intelligent Database Querying W Pedrycz: Lingusitic Data Mining RA Bustos, TD Gedeon: Evaluation of Connectionist Information Retrieval in a Legal Document Collection- Applications Information in Databases and Information Systems: ME Cohen, DL Hudson: Using Linguistic Models in Medical Decision Making JM Mendel, S Murphy, LC Miller, M Martin, N Karnik: The Fuzzy Logic Advisor for Social Judgements: A First Attempt J Zelger, AG de Wet, A-M Pothas, D Petkov: Conceptualisation with GABEK: Ideas on Social Change in South Africa F Herrera, E Lopez, C Manadana, M Rodriguez: A Linguistic Decision Model to Suppliers Selection in International Purchasing L Zerrouki, B Bouchon-Meunier, R Fondacci: Fuzzy System for Air Traffic Flow Management G Michalik, W Mielczarski: A Fuzzy Approach to Contracting Electrical Energy in Competitive Electricity Markets D Ruan: Fuzzy Logic and Intelligent Computing in Nuclear Engineering A Filippidis, LC Jain, NM Martin: Computational Intelligence Techniques in Landmine Detection

On stability of fuzzy systems expressed by fuzzy rules with singleton consequents

Abstract: Addresses the issue of stability of a fuzzy system described by fuzzy rules with singleton consequents. It first presents two canonical forms of a fuzzy system: a parametric expression and a state-space expression. A fuzzy system with singleton consequents is found to be a piecewise-polytopic-affine system. Then the paper gives stability theorems for a discrete-time system as well as for a continuous time system. It also gives a brief survey on the stability issues of fuzzy control systems.

Analysis of direct action fuzzy PID controller structures

Abstract: The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Abstract: From the Publisher: The authors consolidate a wealth of information previously scattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network - classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems.

Analysis and design of hierarchical fuzzy systems

Abstract: In this letter, the hierarchical fuzzy systems are analyzed and designed. In the analysis part, we prove that the hierarchical fuzzy systems are universal approximators and analyze the sensitivity of the fuzzy system output with respect to small perturbations in its inputs. In the design part, we derive a gradient descent algorithm for tuning the parameters of the hierarchical fuzzy system to match the input-output pairs. The algorithm is simulated for two examples and the results show that the algorithm is effective and the hierarchical structure gives good approximation accuracy.

Introduction to Neuro-Fuzzy Systems

Abstract: Fuzzy Systems.- Artificial Neural Networks.- Fuzzy Neural Networks.- Appendix: Case study: A portfolio problem Exercises.

Recurrent neuro-fuzzy networks for nonlinear process modeling

Abstract: A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

Symbolic interpretation of artificial neural networks

Abstract: Hybrid intelligent systems that combine knowledge-based and artificial neural network systems typically have four phases, involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches.

On generating FC/sup 3/ fuzzy rule systems from data using evolution strategies

Abstract: Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and compact (FC/sup 3/). Flexibility, and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical meaning, while compactness is crucial when the number of the input variables increases. However, the completeness and consistency conditions are often violated if a fuzzy system is generated from data collected from real world applications. A systematic design paradigm is proposed using evolution strategies. The structure of the fuzzy rules, which determines the compactness of the fuzzy systems, is evolved along with the parameters of the fuzzy systems. Special attention has been paid to the completeness and consistency of the rule base. The completeness is guaranteed by checking the completeness of the fuzzy partitioning of input variables and the completeness of the rule structure. An index of inconsistency is suggested with the help of a fuzzy similarity which can prevent the algorithm from generating rules that seriously contradict with each other or with the heuristic knowledge. In addition, soft T-norm and BADD defuzzification are introduced and optimized to increase the flexibility of the fuzzy system. The proposed approach is applied to the design of a distance controller for cars. It is verified that a FC/sup 3/ fuzzy system works very well both, for training and test driving situations, especially when the training data are insufficient.

An improved stable adaptive fuzzy control method

Abstract: Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it is shown that a major drawback of that concept, the necessity for new adaptation at every change of the reference signal, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural network domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simulation results.

A new method for constructing membership functions and fuzzy rules from training examples

Abstract: To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the /spl alpha/-cuts of equivalence relations and the /spl alpha/-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.

Fuzzy logic systems for transportation engineering: the state of the art

Abstract: The paper presents a classification and analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Fuzzy logic is shown to be a very promising mathematical approach to modeling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation engineering problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Possibilities are shown regarding the further application of fuzzy logic in this field.

A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering

Abstract: This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster is minimized. A comparison of FCMdd with the relational fuzzy c-means algorithm shows that FCMdd is much faster. We present examples of applications of these algorithms to web document and snippet clustering.

Uncertainty Management for Spatial Data in Databases: Fuzzy Spatial Data Types

Abstract: In many geographical applications there is a need to model spatial phenomena not simply by sharply bounded objects but rather through vague concepts due to indeterminate boundaries. Spatial database systems and geographical information systems are currently not able to deal with this kind of data. In order to support these applications, for an important kind of vagueness called fuzziness, we propose an abstract, conceptual model of so-called fuzzy spatial data types (i.e., a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions. This paper focuses on defining their structure and semantics. The formal framework is based on fuzzy set theory and fuzzy topology.

Hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems

Abstract: We develop a hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems. A Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system and the extended parallel-distributed compensation technique is proposed and formulated for designing the fuzzy model-based controller under stability conditions. The optimal regional-pole assignment technique is also adopted in the design of the local feedback controllers for the multiple TS linear state-space models. The proposed design procedure is as follows: an equivalent fast-rate discrete-time state-space model of the continuous-time system is first constructed by using fuzzy inference systems. To obtain the continuous-time optimal state-feedback gains, the constructed discrete-time fuzzy system is then converted into a continuous-time system. The developed optimal continuous-time control law is finally converted into an equivalent slow-rate digital control law using the proposed intelligent digital redesign method. The main contribution of the paper is the development of a systematic and effective framework for fuzzy model-based controller design with dual-rate sampling for digital control of complex such as chaotic systems. The effectiveness and the feasibility of the proposed controller design method is demonstrated through numerical simulations on the chaotic Chua circuit.

ANN-DT: an algorithm for extraction of decision trees from artificial neural networks

Abstract: Although artificial neural networks can represent a variety of complex systems with a high degree of accuracy, these connectionist models are difficult to interpret. This significantly limits the applicability of neural networks in practice, especially where a premium is placed on the comprehensibility or reliability of systems. A novel artificial neural-network decision tree algorithm (ANN-DT) is therefore proposed, which extracts binary decision trees from a trained neural network. The ANN-DT algorithm uses the neural network to generate outputs for samples interpolated from the training data set. In contrast to existing techniques, ANN-DT can extract rules from feedforward neural networks with continuous outputs. These rules are extracted from the neural network without making assumptions about the internal structure of the neural network or the features of the data. A novel attribute selection criterion based on a significance analysis of the variables on the neural-network output is examined. It is shown to have significant benefits in certain cases when compared with the standard criteria of minimum weighted variance over the branches. In three case studies the ANN-DT algorithm compared favorably with CART, a standard decision tree algorithm.

Granular Computing: Fuzzy Logic and Rough Sets

Abstract: The primary goal of granular computing is to elevate the lower level data processing to a high level knowledge processing. Such an elevation is achieved by granulating the data space into a concept space. Each granule represents certain primitive concept, and the granulation as a whole represents a knowledge. In this paper, such an intuitive idea is formalized into a mathematical theory: Zadeh’s informal words are taken literally as a formal definition of granulation. Such a mathematical notion is a mild generalization of the “old” notion of crisp/fuzzy neighborhood systems of (pre-)topological spaces. A crisp/fuzzy neighborhood is a granule and is assigned a meaningful name to represent certain primitive concept or to summarize the information content. The set of all linear combinations of these names, called formal words, mathematically forms a vector space over real numbers. Each vector is intuitively an advanced concept represented by some “weighted averaged” of primitive concepts. In terms of these concepts, the universe can be represented by a formal word table; this is one form of Zadeh’s veristic constraints. Such a representation is useful; fuzzy logic designs can be formulated as series of table transformations. So table processing techniques of rough set theory may be used to simplify these tables and their transformations. Therefore the complexity of large scaled fuzzy systems may be reduced; details will be reported in future papers.