In this report we want to investigate different methods of Artificial Neural Network optimization. Different local and global methods can be used. Backpropagation is the most common method for optimization. Other methods like genetic algorithm, Tabu search, and simulated annealing can be also used. In this paper we implement GA and BP for ANN.

Combinatorial network optimization theory concerns minimization of connection costs among interconnected components in systems such as electronic circuits. As an organization principle, similar wiring minimization can be observed at various levels of nervous systems, invertebrate and vertebrate, including primate, from placement of the entire brain in the body down to the subcellular level of n...

A new trainable and recurrent neural optimization algorithm, which has potentially superior capabilities compared to existing neural search algorithms to compute high quality solutions of static optimization problems in a computationally efficient manner, is studied. Specifically, local stability analysis of the dynamics of a relaxation-based recurrent neural network, the Simultaneous Recurrent...

Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. The drawbacks fall into the categories of topology selection, optimization and manual tuning. These drawbacks can be partially overcome in a recently proposed technique that reformulates the problem as a convex optimization

Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organ...

Linear programming problem is widely applied in engineering group. And artificial neural network is an effective and practical method and approach for solving linear programming problem of nonlinear convex set constraints in engineering field. Most models of artificial neural network are nonlinear dynamic system. If the objective function of optimization calculation problem is corresponding to ...

The clonal selection mechanism and vaccination strategy of immune system are introduced into particle swarm optimization algorithm in this paper, in order to enhance the ability of global exploration of PSO, avoiding getting into local optimum and improving the accuracy and convergence speed of BP networks. The global Cauchy mutation operator and local Gauss mutation operator are used to improv...

many real water resources optimization problems involve conflicting objectives. in this study, multiobjective genetic algorithm nsga-ii, has been developed for optimization the conjunctive use of surface water and groundwater resources and optimal management of supply and demand of agricultural water. here, optimal allocation of land and water resources to the dominant products in najaf abad pl...