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Neural network-based micropositioning control of smart shape memory alloy actuators
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Information Sciences: an International Journal
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Preface: Adaptive incremental learning in neural networks
Neurocomputing
Cellular particle swarm optimization
Information Sciences: an International Journal
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
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Information Sciences: an International Journal
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Applied Soft Computing
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Information Sciences: an International Journal
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IEEE Transactions on Evolutionary Computation
Selection intensity in cellular evolutionary algorithms for regular lattices
IEEE Transactions on Evolutionary Computation
A new evolutionary search strategy for global optimization of high-dimensional problems
Information Sciences: an International Journal
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Optimal design of constraint engineering systems: application of mutable smart bee algorithm
International Journal of Bio-Inspired Computation
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Editorial: Editorial of the special issue: Online fuzzy machine learning and data mining
Information Sciences: an International Journal
International Journal of Bio-Inspired Computation
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The purpose of current investigation is to engage two efficient evolvable neuro-evolutionary machines to identify a nonlinear dynamic model for a shape memory alloy (SMA) actuator. SMA materials are kind of smart materials capable of compensating any undergo plastic deformations and return to their memorized shape. This fascinating trait gives them versatility to be applied on different engineering applications such as smart actuators and sensors. As a result, modeling and analyzing of their response is an essential task to researchers. Nevertheless, these materials have intricate behaviors that incorporate the modeling with major dilemma and obstacles. In this research, two novel evolvable machines comprised recurrent neural network (RNN) and two novel hybrid heuristic methods nominally cellular automate and Kohonen map assisted versions of The Great Salmon Run (CTGSR and KTGSR respectively) optimization algorithm are developed to find a robust, representative and reliable recursive identification framework capable of modeling the proposed SMA actuator. To elaborate on the acceptable performance of proposed systems, several experimental tests are carried out. Obtained results reveal the promising potential of the evolvable frameworks for modeling the behavior of SMA as a complex real world engineering system. Furthermore, by executing some comparative tests, the authors indicate that both of their proposed hybrid heuristic algorithms outperform the sole version of TGSR as well as some other well-known evolutionary algorithms.