Back propagation (Inteligência artificial)
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Propagation Neural Network
... V. Jaiganesh et. al. [11] proposed a novel intrusion detection system (IDS) by back propagation neural networks (BPN). K. U. Rani [12] proposed a neural network technique for classification of heart ...
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Selection of Proper Activation Functions in Back-propagation Neural Network algorithm for Single-Circuit Transmission Line
... function. Back-propagation neural network (BPNN) is a kind of neural networks, which is widely applied today owing to its effectiveness to solve almost all types of ...
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Performance of ANN using Back Propagation Algorithm for Medical Diagnosis System
... optimal back propagation algorithm in Medical diagnosis ...optimal back propagation algorithm was tested on data set classification problems including the WBC, HGB, PLT, Blood Urea, Cretinine ...
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A COMPARATIVE ANALYSIS OF WEB INFORMATION EXTRACTION TECHNIQUES DEEP LEARNING vs. NAÏVE BAYES vs. BACK PROPAGATION NEURAL NETWORKS IN WEB DOCUMENT EXTRACTION
... Amit Ganatra [7] has proposed initial classification through back propagation algorithm. This paper says initial classification using genetic and neural network algorithm. The goal of this hybrid algorithm ...
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Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier
... Abstract: Problem statement: Image recognition was a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and ...
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A HEURISTIC MOVING VEHICLE LOCATION PREDICTION TECHNIQUE VIA OPTIMAL PATHS SELECTION WITH AID OF GENETIC ALGORITHM AND FEED FORWARD BACK PROPAGATION NEURAL NETWORK
... In this study, a heuristic moving vehicle location prediction algorithm is proposed to predict the vehicles future location from the current location. The proposed technique is based on Genetic Algorithm (GA) and Feed ...
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Palm Vein Recognition System using Directional Coding and Back-propagation Neural Network
... based back-propagation neural network have been performed in this ...stochastic back-propagation, online back-propagation, and batch back- ...
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Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
... Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12– 11, ...
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Classifying Hand Gestures using Back Propagation Neural Network
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Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method
... based back-propagation algorithm is the newly proposed algorithm to optimize the back-propagation neural network ...of back-propagation optimization phase are decided according ...
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Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying
... forward back propagation neural network is chosen as the type of ...The back propagation is a learning algorithm widely used for learning multilayer feed forward neural networks in many ...
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Design and Implementation of a Neural Control System and Performance Characterization with PID Controller for Water Level Control
... This thesis proposes the development of neural network based linearizers to linearize highly nonlinear input-output characteristics of a liquid level control system. Two possible solutions employing two different ...
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Sba Controle & Automação vol.14 número4
... The neural network topology used here is a feedforward one combined with tapped delays. The training process is carried out by using two different techniques: back- propagation and extended Kalman filter. The ...
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Computational complexity evaluation of ann algorithms for image steganalysis
... Abstract - The major growth of information technology is based on the way how the security measures are implemented. Steganography is a technique that implements high level security by hiding a message in a multimedia ...
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Mat. Res. vol.17 número5
... Artificial neural network (ANN) is a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. The ANN model consists of an interconnected ...
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Bol. Ciênc. Geod. vol.19 número4
... Training of BPANN, implemented to find a good mapping function, can be done by an adjustment of the weights between the hidden layer and the output layer to the data set that attempts to decrease the residuals ...
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Estimating Suspended Sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) Models (Case study: Lorestan Province, Iran)
... with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REP Tree for predicting the suspended sediment concentration at ...
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Previsão de vazões médias mensais usando redes neurais nebulosas.
... Recentemente, muitas pesquisas vˆem sendo desenvolvi- das para aplica¸c˜ao de modelos de redes neurais artificiais em previs˜ oes de s´eries temporais por sua habilidade de aprendizado e capacidade de generaliza¸c˜ao, ...
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Detection of Denial of Service Attacks against Domain Name System Using Machine Learning Classifiers
... In this paper, we tried to find the optimized BP network that can effectively detect and classify different DoS attacks against DNS. Our BP neural network has three layers. The number of the units in the input layer is ...
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Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
... error back-propagation supervised learning algorithm is very efficient for many non- linear real time problems, in the case of student classification KSOM – the unsupervised model performs efficiently than ...
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