Optimizing the Structure of Radial Basis Function Networks by Optimizing Fuzzy Inference Systems with Evolution Strategy Internal Report 93{07
نویسنده
چکیده
This report takes advantage of Neural Networks (NN) and Fuzzy Inference Systems (FIS) in order to design a system suited to predict time series. We choose the solution of the Mackey{Glass time delay diierential equation in the chaotic domain as a sample problem. Fuzzy rules are generated from the sample data. The system performance is improved by means of Evolution Strategy (ES). The rules of the FIS are diminished in number due to a heuristic approach. The optimization process is convenient for the structural design of Radial Basis Function Networks (RBFN). The so far predetermined RBFN is further optimized by gradient descent. The system exhibits a good prediction accuracy and generalization.
منابع مشابه
Governor design for hydropower plants by intelligent sliding mode variable structure control
This work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. Considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. By linearizing this mode, a sliding mode controller is designed. The linearized mode is subject to uncertainties. The uncertainties are generated in the process of linear...
متن کاملEvaluation of moving bed biofilm reactor (MBBR) by applying adaptive neuro-fuzzy inference systeme (ANFIS), radial basis function (RBF) and Fuzzy Regression Analysis
The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data an...
متن کاملModeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran)
Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...
متن کاملOn the equivalence of a RBF-Like network to TS fuzzy systems: A GA Approach for TS-Network
Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuuy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the vie...
متن کاملESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS
In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...
متن کامل