An unconventional modelling methodology based on artificial neural networks is proposed to rapidly develop a model from data obtained during different batch experiments. The objective of the global predict time evolution concentrations all species present in reaction medium. For this, recurrent are elaborated estimate particular as function operating parameters and then assembled complex model....

In this paper, a new formulation for discriminative training of HMMs is presented. This formulation uses a properly trained MLP in a simple interconnection with HMMs called “Cascade HMM/ANN Hybrid”. Our training algorithm has simple realization in comparison with other discriminative training for HMMs such as MDI and MMI. We also present a rigid mathematical proof of its convergence. We found t...

Adam Brandenburger and H. Jerome Keisler have recently discovered a two person Russell-style paradox. They show that the following configurations of beliefs is impossible: Ann believes that Bob assumes that Ann believes that Bob’s assumption is wrong. In [7] a modal logic interpretation of this paradox is proposed. The idea is to introduce two modal operators intended to represent the agents’ b...

review and classification of electric load forecasting (lf) techniques based on artificial neuralnetworks (ann) is presented. a basic anns architectures used in lf reviewed. a wide range of annoriented applications for forecasting are given in the literature. these are classified into five groups:(1) anns in short-term lf, (2) anns in mid-term lf, (3) anns in long-term lf, (4) hybrid anns inlf,...

This paper investigates artificial neural network (ANN) based modelling and compensation of nonlinear friction which is a major cause of performance degradation in servo mechanisms. Different friction modelling and compensation schemes have been reviewed and neural network based hybrid compensation methods are proposed and experimentally tested on a direct drive servo mechanism. Inertial dynami...

Review and classification of electric load forecasting (LF) techniques based on artificial neural networks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANN oriented applications for forecasting are given in the literature. These are classified into five groups: (1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs in...

Objectives: To propose a novel hybrid control plan to enhance the power quality (PQ) of distributed energy sources (DERs) such as solar (SE), wind (WE), and battery storage system (BEES) technologies. Method: A BEES, PV farm, farm are integrated into grid supply home loads. The ABMO-ANN is method that combines Hybrid Barnacles Mating Optimization Algorithm (HBMO) Artificial Neural Network (ANN)...

A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. Abstract This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN appro...

in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear ...