This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system’s ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suit...

The goal of this paper is to compare and analyze the forecasting performance of two artificial neural network models (i.e., MLP (multi-layer perceptron) and DNN (deep neural network)), and to conduct an experimental investigation by data flow, not economic flow. In this paper, we investigate beyond the scope of simple predictions, and conduct research based on the merits and data of each model,...

Abstract In this study, Au+Au collisions with an impact parameter of fm at \sqrt{s_{NN}} = 200 GeV are simulated using the AMPT model to provide preliminary final-state information. After transforming information into appropriate input data (the energy spectra charged hadrons), we construct a multi-layer perceptron (MLP) and convolutional neural network (CNN) connect...

Linear regression methods have been applied for decades and are well known and understood (Millionis, A.E. and T.D. Davies, 1994; Robeson, S.M. and D.G. Steyn, 1990; Ryan, W.F. 1995; Shi, J. P. and R.M. Harrison, 1997). However, there are numerous environmental processes that exhibit significant non-linear behaviour. Advances in the field of Artificial Neural Networks (ANN) in the late 1980s po...

In this paper an adaptable acoustical architecture in a multilingual TTS system is presented. The whole architecture is designed to be a data-driven system. Modules comprising text preprocessing, grapheme-to-phoneme conversion, lexical stress detection, OOV-handling, symbolic prosody prediction, acoustic prosody prediction and unit selection with concatenation use machine learning techniques es...

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) multi-layered perceptron artificial neural network (mlp-NN), over Southwestern United...

Slope stability is the most important stage in stabilization process for different scale slopes, and it dictated by factor of safety (FS). The FS a relationship between geotechnical characteristics slope behavior under various loading conditions. Thus, application an accurate procedure to estimate can lead fast precise decision during process. In this regard, using computational models that be ...

Ensuring the performance of wastewater treatment processes is important to guarantee that final treated quality safe for reuse. However, bacterial concentration present along different stages process not easily measured routinely plant operators. In this paper, a moving horizon sensing approach based on neural networks proposed estimate in sampled plant. Due difficulties measure bacteria and la...

In this work a modification was made on the algorithm of Artificial Neural Networks (NN) Training of the Multilayer Perceptron type (MLP) based on multi-objective optimization (MOBJ), to increase its computational efficiency. Usually, the number of efficient solutions to be generated is a parameter that must be provided by the user. In this work, this number is automatically determined by an al...

This paper presents experiments that compare the performances of several versions of a Regional-Fuzzy Representation (RFR) developed for Cursive Handwriting Recognition (CHR). These experiments are conducted using a common Neural Network (NN) classifier, namely a MultiLayer Perceptron (MLP) trained with backpropagation. Results are given for Sections 1a (isolated digits), 1c (isolated lower-cas...