Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Journal of Global Optimization
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Self Generating Metaheuristics in Bioinformatics: The Proteins Structure Comparison Case
Genetic Programming and Evolvable Machines
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Landscapes, learning costs, and genetic assimilation
Evolutionary Computation
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
Empirical investigation of the benefits of partial lamarckianism
Evolutionary Computation
Design of fractional-order PIλDµ controllers with an improved differential evolution
Engineering Applications of Artificial Intelligence
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification
International Journal of Applied Evolutionary Computation
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Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis.