Neuro-Optimizer: A New Artificial Intelligent Optimization Tool and Its Application for Robot Optimal Controller Design


The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrection
strategy supported by a recurrent neural network for finding a near optimal solution of a given
objective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimization
problems and some types of ANNs such as Hopfield network and Boltzmann machine have been applied in
combinatorial optimization problems. However, ANNs cannot optimize continuous functions and discrete
problems should be mapped into the neural networks architecture. To overcome these shortages, we introduce a
new procedure for stochastic optimization by a recurrent artificial neural network. The introduced neurooptimizer
(NO) starts with an initial solution and adjusts its weights by a new heuristic and unsupervised rule to
compute the best solution. Therefore, in each iteration, NO generates a new solution to reach the optimal or
near optimal solutions. For comparison and detailed description, the introduced NO is compared to genetic
algorithm and particle swarm optimization methods. Then, the proposed method is used to design the optimal
controller parameters for a five bar linkage manipulator robot. The important characteristics of NO are:
convergence to optimal or near optimal solutions, escaping from local minima, less function evaluation, high
convergence rate and easy to implement.


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