Principles of Artificial Neural Networks: 3rd Edition (Advanced Series in Circuits & Systems) (Advanced Series in Circuits and Systems)
Daniel Graupe
Language: English
Pages: 500
ISBN: 9814522732
Format: PDF / Kindle (mobi) / ePub
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.
This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.
The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks.
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Neural Networks weights = (rand(1,inputsNumber) -0.5 )* range + offset; elseif strcmp(weightCat, ‘const’) weights = ones(1,inputsNumber) * defaultWeight; else error(‘error paramters when calling createDefaultHopfield.m’); return; end aNeuron.weights = weights; aNeuron.z = 0; aNeuron.y = 0; aLayer.neurons = [aLayer.neurons, aNeuron]; aLayer.weights = [aLayer.weights; weights]; end hopfield = aLayer; File #6 function testingData = getHopfieldTestingData(trainingData, numberOfBitError,.
Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN 978-981-4522-73-1 Printed in Singapore June 25, 2013 15:33 Principles of Artificial Neural Networks (3rd Edn) Dedicated to the memory of my parents, to my wife Dalia, to our children, our daughters-in-law and our grandchildren It is also dedicated to the memory of Dr. Kate H. Kohn v ws-book975x65 June 25, 2013 15:33 Principles of Artificial Neural Networks.
Lambda=3.0; int i,n2; int numit=4000; int cityno=15; // cin>>cityno; //No. of cities float input_vector[Maxsize*Maxsize]; time_t start,end; double dif; start = time(NULL); srand((unsigned)time(NULL)); //time (&start); n2=cityno*cityno; outFile<<"Input vector:\n"; for (i=0;i 1958). (b) The Artron (Statistical-Switch-based neuronal model) due to R. Lee (1950s). It is a decision making automaton, not having a network architecture. It can be viewed as a statistical neuron-automaton (pre-Perceptron). It lies outside the scope of this text. (c) The Adaline (Adaptive Linear Neuron, due to B. Widrow, 1960). This artificial neuron is also known as the ALC (adaptive linear combiner), the ALC being its principal component. It is a single neuron, not a network. (d) The Madaline. 1958). (b) The Artron (Statistical-Switch-based neuronal model) due to R. Lee (1950s). It is a decision making automaton, not having a network architecture. It can be viewed as a statistical neuron-automaton (pre-Perceptron). It lies outside the scope of this text. (c) The Adaline (Adaptive Linear Neuron, due to B. Widrow, 1960). This artificial neuron is also known as the ALC (adaptive linear combiner), the ALC being its principal component. It is a single neuron, not a network. (d) The Madaline.