A simply identified Sugeno-type fuzzy model via double clustering
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Advanced self-organizing polynomial neural network
Neural Computing and Applications
Evolutionary design of fuzzy rule base for nonlinear system modeling and control
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Hi-index | 0.00 |
We discuss a new design of group method of data handling (GMDH)-type neural network using evolutionary algorithm. The performances of the GMDH-type network depend strongly on the number of input variables and order of the polynomials to each node. They must be fixed by designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems we employed evolutionary algorithms. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. The appropriate information of each node are evolved accordingly and tuned gradually throughout the GA iterations. By the simulation results, we can show that the proposed networks have good performance.