Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.

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Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Learning soft computing control strategies in a modular neural network architecture

Organic Carbon (OC) is a soil property that describes the amount of carbon broken down from plants or other life forms stored in the soil below. Traditionally to quantify organic carbon content, a soil sample is collected and analyzed in a laboratory. This is a time-consuming and expensive process. Recently there has been interest in finding cost-efficient solutions to predicting organic carbon using remotely sensed data. Australia-wide readings of organic carbon and associated input factors were used in the study. The input factors included climate, landform, Landsat Multi-Spectral Scanner bands, lithology and land use. Experiments have been undertaken using Artificial Neural Networks (ANNs), Multiple Linear Regression (MLR) and ensembles of ANNs. A dynamic ensemble combination model is presented. The ensemble assesses the performance of the ensemble members on individual cases and weights each ensemble member relative to their performance on similar cases. The weighting scheme is dynamic, changing for each case in the testing set. The ensemble output is derived from the combination of weighted ensemble members. The results encourage further research into estimating organic carbon using ensembles.

Dynamic ensemble approach for estimating organic carbon using computational intelligence

Modelowanie i korekcja wybranych systemów nieliniowych z zastosowaniem metod analitycznych i aproksymatorów neuronowych

This paper addresses the problem of the identification of nonlinear dynamic systems using modularly structured neural network with the new learning algorithm for the learning of both gating and expert networks weights. Here we start with the standard learning procedure for such networks in the sense that the problem of learning is formulated and treated as a mixture estimation problem in which the log-likelihood function should be maximised. But, as opposite to the established methods for modular networks we combine the gradient type of learning for gating weights with a least-squares algorithm for the learning of expert networks weights, and the experts are simple one-layer nets with a single linear output unit, The very result of such an approach is a simple structured modular network with improved learning and it seems with good capabilities for the identification of general nonlinear dynamic systems. The identifications of a discrete-time nonlinear system corrupted with noise and of a real world system are presented. Later process represents the identification of the positioning of a car engine throttle valve from real data set (3000 samples of measured noisy data).

V. Kecman

Papers

System identification using modular neural network with improved learning