Nonlinear programming problem solving based on winner take all emotional neural network for tensegrity structure design

In this paper, a tensegrity structure (TS) design is formulated as a nonlinear programming (NLP) problem, and a winner-take-all artificial emotional neural network (WTA-ENN) is proposed to solve the resulting NLP. The main feature of proposed WTA-ENN is related to low number of learning weights and simplicity of its learning rules that make it a suitable model for complicated TS design problems. Numerical results indicate that WTA-ENN can effectively solve NLP problem obtained from basic module of a typical TS Tower. The proposed method can be effectively used in architectural, structural and robotics design.

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