Neural networks, such as multi-layer perceptron (MLP) networks which converge slowly, have been applied for tra c and congestion control in ATM networks. In this paper, we present a Connection Admission Control (CAC) scheme using modular and hierarchical neural networks for predicting the resulting cell loss rate (CLR) when calls are accepted. The fast learning and accurate predictions obtained...

This paper presents the results of modelling to predict the effluent biological oxygen demand (BOD5) concentration for primary clarifiers using a hybridisation of unsupervised and supervised artificial neural networks. The hybrid model is based on the unsupervised self-organising map (SOM) whose features were then used to train a multi-layered perceptron, feedforward back propagation artificial...

A novel neural-network based technique is described for the remote condition-monitoring of an in-service gas-turbine flowmeter. The method uses a C language implementation of a modified multi-layer perceptron (MLP) neural networks, which enables detection of the accumulation of contaminating material on the rotor blades that could lead to changes in meter-factor and loss of calibration.

This paper reviews various optimization techniques available for training multi-layer perception (MLP) artificial neural networks for compression of images. These optimization techniques can be classified into two categories: Derivative-based and Derivative free optimization. The former is based on the calculation of gradient and includes Gradient Descent, Conjugate gradient, Quasi-Newton, Leve...

To analyze large sets of digital micrographs from high-throughput screening studies with constant accuracy, advanced image processing algorithms are necessary. In the literature, systems have been proposed applying modelbased fitting algorithms, morphological operators and artificial neural networks (ANN). Because single approaches show limited performance, we propose a hybrid system that combi...

A functional relationship between two variables, applied mass to a weighing platform and estimated mass using Multi-Layer Perceptron Artificial Neural Networks is approximated by a linear function. Linear relationships and correlation rates are obtained which quantitatively verify that the Artificial Neural Network model is functioning satisfactorily. Estimated mass is achieved through recallin...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

Background: Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficul...

Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks model...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...