in this paper, lift and drag coefficients were numerically investigated using numeca software in a set of 4-digit naca airfoils. two metamodels based on the evolved group method of data handling (gmdh) type neural networks were then obtained for modeling both lift coefficient (cl) and drag coefficient (cd) with respect to the geometrical design parameters. after using such obtained polynomial n...

This paper proposes an optimization strategy which is based on neural networks and genetic algorithms to calculate the optimal values of gas injection rate and oil rate for oil production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gaslifted wells. For both cases an objective function is maximized to reduce production cost. The p...

In this paper we derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularization parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative combinatorial search among the relevant subsets of an initial neural network architecture by emp...

We present neural inverse space mapping (NISM) optimization for electromagnetics-based design of microwave structures. The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a space mapping algorithm. NISM optimization does not require up-front EM simulations, multipoint parameter extraction, or frequency mapping. It employs a simple sta...

Convex optimization techniques are widely used in the design and analysis of communication systems and signal processing algorithms. In this paper a novel recurrent neural network is presented for solving nonlinear strongly convex equality constrained optimization problems. The proposed neural network is based on recursive quadratic programming for nonlinear optimization, in conjunction with ho...

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using c...

Evolutionary multi-objective optimization of spiking neural networks for solving classification problems is studied in this paper. By means of a Paretobased multi-objective genetic algorithm, we are able to optimize both classification performance and connectivity of spiking neural networks with the latency coding. During optimization, the connectivity between two neurons, i.e., whether two neu...

When the future information for an optimization model is not complete, the model tends to incorporate such uncertainties as some assumptions on the coefficients. As time passes and more precise information is accumulated, the initial optimal solution may no longer be optimal, or even feasible. At this point, model builders want to modify the assumed and controllable coefficients to obtain the d...

We present a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent ne...

this study investigates the oil extraction from pistacia khinjuk by the application of enzyme.artificial neural network (ann) and adaptive neuro fuzzy inference system (anfis) were applied formodeling and prediction of oil extraction yield. 16 data points were collected and the ann was trained with onehidden layer using various numbers of neurons. a two-layered ann provides the best results, us...