In this paper, a new Transform Domain implementation of the well known Multilayer Perceptron Neural Network is presented. With the Transform Domain implementation, the input of the Neural Network can be represented in a more compact manner and the elements of the input vector become uncorrelated. The new Transform Domain Multilayer Perceptron (TDMLP) Neural Network is applied for the problem of...

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it incapable classifying linearly inseparable patterns. A new era research started 1986, when backpropagation (BP) algorithm was rediscovered for training multilayer perceptron (MLP) model. An MLP with a large number hidden nodes can function as universal approximat...

The significance of accurate heating load (HL) approximation is the primary motivation this research to distinguish most efficient predictive model among several neural-metaheuristic models. proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), i...

Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become de-facto standard for HSI classification. It seems that traditional networks such as multi-layer perceptron (MLP) are not competitive However, in this study, we try prove MLP can achieve good performance if it is properly des...

Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions optimizing MLP time series forecasting. This study uses autoregressive integrated moving average (ARIMA) with method. These methods were to predict the Air Pollutant Index (API) Malaysia's central region where represent...

Using ANN algorithms to address optimization problems has substantially benefited recent research. This study assessed the heating load (HL) of residential buildings’ heating, ventilating, and air conditioning (HVAC) systems. Multi-layer perceptron (MLP) neural network is utilized in association with MVO (multi-verse optimizer), VSA (vortex search algorithm), SOSA (self-organizing self-adaptive...

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...

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

Loss rate is one of the most important Quality of Service (QoS) requirements in a packet communication network carrying multimedia traac. This paper presents a method for estimating loss rates as a function of a feature vector, x, based on a maximum likelihood principle. Two backpropagation networks, a multi-layer perceptron (MLP) network and a radial basis function (RBF) network, are applied t...