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    Artificial Neural Networks

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    Artificial neural networks (ANNs) were built to model the brain for the purpose of solving the problems humans alone cannot as well as to advance, artificial intelligence. To approximate organic beings and gain great computational power, to become a technological hybrid between sentient beings and advanced electronics; they are the future of advanced robotics. They can be used in miscellaneous fields such as speech recognition, prediction of stocks, weather and so on. Artificial neural networks (ANNs)

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    Artificial Neural Networks

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    Artificial Neural Networks Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize

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    4 Artificial Neural Networks The study of artificial neural networks help us understand the brain, perception and human cognition and lead to the development of systems with value-added functionality to solve difficult problems. This means the development of neural networks is inspired by the human capabilities for learning, understanding and performing complex tasks with easiness and accuracy. To reference something, simply recognizing human faces, kicking a football or fetching a glass of water

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    Artificial Neural Networks Report

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    Artificial Neural Networks Report Artificial Neural Networks 1. Introduction Artificial Neural Networks are computational models inspired by an animal's central nervous systems (brain) that has the ability of machine learning. Artificial neural networks are generally presented as systems of interconnected "neurons" which can compute values from inputs (from wikipedia). 2. Training an Artificial Neural Network The network is ready to be trained if it had been structured to service

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    Neural Network Concept in Artificial Intelligence Abstract Since the 1980's there have been renewed research efforts dedicated to neural networks. The present interest is largely due to the difficult problems confronted by artificial intelligence, and due to the deeper understanding of how the brain works, the recent developments in theoretical models, technologies and algorithms. One motivation of neural network research is the desire to build a new breed of powerful computers to solve a variety

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    capacitance value. This study presents a novel technique for tracking maximum efficiency point in the entire operating range of WRIM using Artificial Neural Network (ANN). The data for ANN training were obtained on a three phase WRIM with dynamic capacitor control and rotor short circuit at different speed and load torque values. Approach: A novel nueral network model based on back-propagation algorithm has been developed and trained for determining the maximum efficiency of the motor with no prior

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    0.1 L/G = 0.07 CO2-NaOH-H2O: L/G = 0.16 0.385 0.540 0.366 0.725 CONCLUSION This paper investigates modeling strategy by artificial neural networks for the non-linear dynamic processes of a cyclone scrubber. The three layer feed-forward neural network (3-FFNN) has been chosen for neural network modeling. The comparison between the simulation results of the neural network and experimental data has been discussed to show the validity of the proposed model. The comparison illustrates that the accuracy

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    multi-layered Artificial Neural Networks(ANN) for classification of the age group of a person. The result is classified into total eight groups of age-ranges. The motivation for our work lies in various... ... middle of paper ... ...res", The 36th Intl. Conf. on Acoustics, Speech and Signal Processing, (ICASSP 2011), Prague, Czech Republic, May, 2011. [13] Yinyin Liu, Janusz A. Starzyk, Zhen Zhu, “Optimizing Number Of Hidden Neurons in Neural Networks”, http://www.ohio.edu/people/starzykj/network/Research/Papers/Recent%20conferences

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    SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL NETWORKS Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org sundern@telkom.co.za theo@wpo.mlsultan.ac.za Electronic Engineering Department, M L Sultan Technikon, P O Box 1334, Durban, South Africa. ABSTRACT Crime and corruption have become rampant today in our society and countless money is lost each year due to white collar crime, fraud, and embezzlement. This

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    words from speech stream; however, there is now a growing disagreement on its existence in all children (Goldfield & Reznick, 1990; Ganger & Brent, 2004). The aim of the present essay is to evaluate the ability of two theories, namely the Artificial Neural Network (ANN) and Dynamical Systems theory (DST), to explain the issues underlying the lexical development and vocabulary spurt. This essay provides an overview of both theories and compares their strengths and weaknesses in their explanation of

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