This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork -based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components on-linely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. 1 Introduction System modeling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with ill-define...

This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational power, etc., can be applied to both models directly. It is of interest to observe that twomodels stemming from different origins turn out to be functional equivalent.

This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise differentiable format, such as difference equations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules obtained from human experts, or automatically derive the fuzzy if-then rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller. 1 Introduction Fuzzy controllers (FC's) have recently found various applications in industry as well as in household appliances. For com...

Intelligent energy management has become one of the major research fields in electrical engineering. It constitutes an important tool for efficient planning and operation of power systems and its significance has been intensifying particularly, because of the recent movement towards open energy markets and the need to assure high standards on reliability. Hybrid neuro-fuzzy paradigms have recently gained a lot of interest in research and application. In this chapter, we discuss two neuro-fuzzy paradigms for intelligent energy management. In the first approach, a neural network learning algorithm is used to fine tune the parameters of a Mamdani and Takagi Sugeno Fuzzy Inference System (FIS). Mamdani FIS is used to predict the energy demand and the TakagiSugeno FIS is used to predict the reactive power flow. In the second approach, fuzzy if-then rules were embedded into an Artificial Neural Network (ANN) learning algorithm (fuzzy-neural network) to achieve improved performance for short-term load forecast. The performance of the different neuro-fuzzy paradigms were tested using real world data and compared with a direct neural network and FIS approach. The different performance results obtained clearly demonstrates the importance of the proposed techniques for intelligent energy management.

The goal of this work is to compare fuzzy, neural network and neuro-fuzzy approaches to the control of mobile robots. The first part of this paper is devoted to the formal framework of fuzzy controllers. Results of an example of their use for a mobile robot are discussed. As an experimental platform, the Khepera mobile robot is used. The same example is studied using artificial neural networks. For that purpose, fundamentals of artificial neural networks are outlined. Similarities and differences between fuzzy systems and neural networks are discussed as well as the respective advantages and drawbacks, and reasons for merging these two approaches are developed. Three models of fuzzy neurons, the learning methods and an architecture of neuro-fuzzy controller are presented. A learning procedure for the controller is described. To conclude, the application of a neuro-fuzzy controller on Khepera is discussed. 1 INTRODUCTION The word robot (robota) has Slavic origins and means work. Robots...

This paper describes a new approach for reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. Energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on development of a correlation between accelerations of the building (controller input) and voltage applied to the MR damper (controller output). This correlation forms the basis for development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Soft Computing (SC) introduced by Lotfi Zadeh [1] is an innovative approach to construct computationally intelligent hybrid systems consisting of Artificial Neural Network (ANN), Fuzzy Logic (FL), approximate reasoning and derivative free optimization methods such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various hybrid systems conceptually and evaluate their performance comparatively. It has been over a decade since HIS were first applied to solve complicated problems. In this paper, we first aim at classifying state--of--the--art intelligent systems, which have evolved over the past decade in the HIS community. Some theoretical concepts of ANN, FL and Global Optimization Algorithms (GOA) namely GA, SA and TS are also presented. We further attempt to summarize the work that has been done and present the current standing of our vision on HIS and future research directions.

The goal of this work is to make an analysis of the performances of a fuzzy controller and a comparative study of fuzzy control algorithms with a conventional control approach (PID) in the case of linear dynamic process control. This comparative study is made using computer simulation. The first part is devoted to the formal framework of the theory of fuzzy sets and fuzzy controllers. The second part of this paper is a description of a simulated system, and a presentation of simulated controllers. In the second part, fuzzy controller is examined in details. A sensitivity of the fuzzy logic controller to design parameters, different shapes and superposition of membership functions, is tested. Moreover, the simulations are done for the different types of fuzzy reasoning and defuzzification methods. Ecole Polytechnique Fédérale de Lausanne Département d'Informatique Laboratoire de Microinformatique 2 1. Introduction The humans, when making decisions tend to work with vague or impreci...

The goal of this work is to propose a learning procedure for fuzzy systems. Fuzzy systems are able to treat uncertain and imprecise informations. They have a capability to express knowledge in the form of linguistic rules. Their drawbacks are caused mainly by the difficulty of defining accurate membership functions and lack of a systematic procedure for the transformation of the expert knowledge into the rule base. Neural networks have the ability to learn but both knowledge extraction and knowledge representation are difficult. First, a neuro-fuzzy architecture is proposed. A learning procedure based on the stochastic approximation method is described. The methodology is the supervised learning method developed in the field of neural networks. In order to discuss the validity of the proposed method, three numerical examples are presented and it is shown that the proposed neuro-fuzzy system have the ability to learn. It is applied to the obstacle avoidance problem of a mobile robot. As...

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.