Fuzzy-neural Adaptation through Evolutionary Exploration
نویسندگان
چکیده
A general adaptation through 'exploration' approach for controlling discrete event systems is presented. In this approach optimal controllers for a few operating conditions are determined (exploration) and an on-line adaptation module is trained on this data for generalization. A machinerepair example is formulated to illustrate the general control method. The on-line adaptation mechanism is applied to this example, and the use of fuzzy logic and artificial neural networks for such adaptation is compared.
منابع مشابه
Modeling and Control of Discrete Event Dynamic Systems: a Simulator-based Reinforcement-learning Paradigm
A general reinforcement-learning approach for controlling discrete event systems is presented. A machine-repair example is formulated: (1) to describe and explain the DEVS formulation, and (2) to illustrate the general control method. Modified gradient learning methods and evolutionary programming methods are compared for the purpose of optimizing the controller. An on-line adaptation method is...
متن کاملEvoNF: A Framework for Optimization of Fuzzy Inference Systems Using Neural Network Learning and Evolutionary Computation
Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...
متن کاملSoft case-based reasoning
The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief sum...
متن کاملTen years of genetic fuzzy systems: current framework and new trends
Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximat...
متن کاملIntrusion detection using a fuzzy genetics-based learning algorithm
Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems ...
متن کامل