Use Of Modular Neural Networks Research Articles

Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks

The complexity of problems has led to a shift toward the use of modular neural networks in place of traditional neural networks. The number of inputs to neural networks must be kept within manageable limits to escape from the curse of dimensionality. Attribute division is a novel concept to reduce the problem dimensionality without losing information. In this paper, the authors use Genetic Algorithms to determine the optimal distribution of the parameters to the various modules of the modular neural network. The attribute set is divided into the various modules. Each module computes the output using its own list of attributes. The individual results are then integrated by an integrator. This framework is used for the diagnosis of breast cancer. Experimental results show that optimal distribution strategy exceeds the well-known methods for the diagnosis of the disease.

Hybrid Modelling of Fermentation Processes: A Study on the Use of Modular Neural Networks for Modelling Cells Reaction Kinetics

Hybrid models of fermentation processes usually employ Artificial Neural Networks (ANNs) for modelling cells reaction kinetics. The most employed network types are the Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. The main objective in this work was to investigate the applicability of modular neural networks for modelling cell reaction kinetics in bioreactors. The study was supported with simulations of a wastewater treatment process using the ASM model number 2d. The main results show that modular networks if trained with the expectation maximisation algorithm are able to discriminate between pathways and to develop expertise in describing the different pathways. However no advantage was observed in terms of the ratio modelling accuracy/number of parameters

Power system stabilization based on modular neural network architecture

Backpropagation neural networks have recently been applied to problems in power system stabilizer modeling. When trained to respond differently to different operating conditions, these networks tend to produce interference between conflicting solutions. In recent years, modular neural network architectures have been used for problems in system identification and control. These networks learn different aspects of a problem by partitioning the data space into several different regions and are less susceptible to interference than backpropagation networks. This paper investigates the use of modular neural networks for power system stabilizer modeling. Simulation studies are performed to compare the modular neural network model of a power system stabilizer against a backpropagation model and a conventional power system stabilizer model.

PD classification by a modular neural network based on task decomposition

The use of modular neural network (MNN) for the recognition of partial discharge (PD) sources has been investigated. Three phase related quantities, the PD pulse counts, the average and maximum discharge magnitudes form the feature vector of a discharge signal. The MNN consists of 5 sub-networks with identical structure and a maximum selector. Each subnetwork is assigned the task to recognize a particular PD source. Compared with a single neural network which is trained to recognize all PD sources, the MNN has a higher training ability, faster rate of convergence and better recognition rate.