A new method for the identification of Hammerstein model
Automatica (Journal of IFAC)
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Quantum-Behaved Particle Swarm Optimization with Mutation Operator
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
An Improved Particle Swarm Optimization with Mutation Based on Similarity
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
IEEE Computational Intelligence Magazine
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Combining the good features of particle swarm optimization (PSO) and artificial immune system (AIS) we propose a new Immunized PSO (IPSO) algorithm. This algorithm is used to identify generalized Hammerstein model by employing functional link artificial neural network (FLANN) architecture for the nonlinear static part and an adaptive linear combiners for the linear dynamic part of the model. Simulation study of few benchmark Hammerstein models is carried out through simulation study and the results obtained are compared with those obtained by standard PSO and AIS based method. Comparison of results demonstrate superior performance of the proposed methods over its PSO and AIS counterpart in terms of response matching, accuracy of identification and convergence speed achieved.