Preferred design of recurrent neural network architecture using a multiobjective evolutionary algorithm with un-supervised information recruitment: a paradigm for modeling shape memory alloy actuators

Abstract

Shape memory alloys (SMAs) are able to compensate any undergoing plastic deformations and return to their memorized shape. Such a behavior persuades industrialists to use them for different engineering applications, as smart actuators and sensors. Because of their vast applications, it is crucial to engineers to develop effective identification tools capable of simulating the behavior of SMAs. However, SMA actuators have complex and hysteric behavior that in turn obstructs the modeling process. The motivation behind the current study emanates in the pursuit of developing efficient prediction tools for effective modeling of SMA actuators. Actually, after several experiments and software simulations, the authors develop a hybrid intelligent tool which takes advantage of the self-organizing Pareto based evolutionary algorithm (SOPEA) and simultaneous recurrent neural network (SRNN), as a black-box model, to automatically identify the behavior of SMA. SOPEA is a multiobjective evolutionary algorithm which is based on the concepts of survival of the fittest, non-dominated sorting and information recruitment. The information recruitment is guaranteed by applying an un-supervised neuro computing technique, i.e. adaptive self organizing map (ASOM) with conscience mechanism. ASOM is an un-supervised network that assists SOPEA to recognize the non-dominated patterns and produce further non-dominated solutions. Together with the structure of SOPEA, the authors follow a comprehensive preference-based strategy to exploit the desired regions in the Pareto front. This occurs through introducing deliberate reference points. The outcome method is applied to the design of SRNN for modeling the SMA actuator. It is demonstrated that the designed optimization tool can show acceptable performance for the present case study within the imposed computational budget. Besides, through a rigorous experimental procedure, it is indicated that by applying an efficient artificial system, the behavior of SMA can be identified without any specific knowledge of the physical conditions and governing equations.