International Journal of Approximate Reasoning
Applying genetics to fuzzy logic
AI Expert
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Genetic algorithms for fuzzy controllers
AI Expert
A learning algorithm of fuzzy neural networks with triangular fuzzy weights
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Manufacturing process control through integration of neural networks and fuzzy model
Fuzzy Sets and Systems
Fuzzy neural networks with application to sales forecasting
Fuzzy Sets and Systems
Fuzzy Sets and Systems
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
The inventory management system for automobile spare parts in a central warehouse
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Computers and Industrial Engineering
Hi-index | 12.05 |
Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if-then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.