However‚ our primary goal is finish the project with the satisfaction of accomplishing something that was at a level higher than what we have expected. The resulting performance is based upon several main factors: how well we train our Artificial Neural Network‚ how representative the speech features are‚ and how consistent the data are. The ANN determines how accurate and precise our comparison will be with our stored template. If the ANN finds the speaker in the template‚ we’ll be able to display
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Neural Network For Optimization An artificial neural network is an information or signal processing system composed of a large number of simple processing elements‚ called artificial neurons or simply nodes‚ which are interconnected by direct links called connections and which cooperate to perform parallel distributed processing in order to solve a desired computational task. The potential benefits of neural networks extend beyond the high computation rates provided by massive parallelism. The neural
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Having established the basis of neural nets in the previous chapters‚ let’s now have a look at some practical networks‚ their applications and how they are trained. Many hundreds of Neural Network types have been proposed over the years. In fact‚ because Neural Nets are so widely studied (for example‚ by Computer Scientists‚ Electronic Engineers‚ Biologists and Psychologists)‚ they are given many different names. You’ll see them referred to as Artificial Neural Networks (ANNs)‚ Connectionism or Connectionist
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A Computational Methodology for Modelling the Dynamics of Statistical Arbitrage Andrew Neil Burgess Decision Technology Centre Department of Decision Sciences A thesis submitted to the University of London for the degree of Doctor of Philosophy UNIVERSITY OF LONDON LONDON BUSINESS SCHOOL 1 October 1999 To my parents‚ Arnold and Carol. © A. N. Burgess‚ 1999 2 3 Acknowledgements Thanks to my supervisor‚ Paul Refenes‚ for bringing me to LBS‚ keeping me in bread and
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Issues from the use of ICT Why do issues arise with the use of ICT? Who has the responsibility of addressing these issues? Tensions can arise through unintended or deliberate actions or opinions of some of the stakeholders. As a result‚ the stakeholders develop‚ control‚ use or affected by the use of ICT have responsibility both to investigate and real and potention negative efftcs and to eliminate the lessen them as much as possible. Computer gaming What are the symptoms of computer addiction
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to psychological therapies. History of the theories The neural network model attempts to explain that which is known about the retention and retrieval of knowledge. Neural network models have been examined for a number of years. In the mid 1940 ’s and 1950 ’s the first of the network models began to appear. These publications introduced the first models of neural networks as computing machines‚ the basic model of a self-organizing network (Arbib‚ 1995). In 1943 McCulloch and Pitts published
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AI in optical character recognition Artificial intelligence (AI) is the intelligence of machines. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. There are different fields under optical character recognition. Intelligent character recognition Handwriting recognition Automatic number plate recognition In computer science‚ intelligent
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SEGMENTATION A Neural Network Application Jonathan Z. Bloom University of Stellenbosch‚ South Africa Abstract: The objective of the research is to consider a self-organizing neural network for segmenting the international tourist market to Cape Town‚ South Africa. A backpropagation neural network is used to complement the segmentation by generating additional knowledge based on input–output relationship and sensitivity analyses. The findings of the self-organizing neural network indicate three clusters
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analytical models are valid for ideal cases. • Real world problems exist in a non-ideal environment. 3 What is Soft Computing? (Continued) • The principal constituents‚ i.e.‚ tools‚ techniques‚ of Soft Computing (SC) are – Fuzzy Logic (FL)‚ Neural Networks (NN)‚ Support Vector Machines (SVM)‚ Evolutionary Computation (EC)‚ and – Machine Learning (ML) and Probabilistic Reasoning (PR) 4 Premises of Soft Computing • The real world problems are pervasively imprecise and uncertain • Precision
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In data clustering‚ we choose 5 artificial datasets and 9 UCI datasets to test the performance of ARMOPSO. Table 4 shows the specific information of these datasets. Table 4 The information of datasets used in data clustering. Datasets Number of data objects Dimension of a data object Number of clusters Art2 data3 data1 Art1 data9 Iris Wine Glass Thyroid Ecoli bupaLD Cancer pimaIndians CMC 250 400 400 600 600 150 178 214 215 336 345 683 768 1473 3 2 2 2 3 4 13 9 5 7 6 9 8 9 5 3 2 4 3 3 3 6 3 8 2 2
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