Backpropagation
Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation.
The Handbook of Brain Theory and Neural Networks
... backpropagation as a tool for studying actual networks in the brain . The argument here is that backpropagation provides a means for the computational neurosci- entist to adjust the parameters within a given neural network ar ...
Artificial Neural Networks
... Backpropagation ( A.32 ) As we mentioned earlier , backpropagation is the most common algorithm used to train feedforward systems . It has several advantages , listed in Table A.3 ... Backpropagation Alternatives to Backpropagation.
Neural Network Control Of Robot Manipulators And Non-Linear Systems
... Backpropagation Algorithm . Verify the matrix formulation of backpropagation in Equations ( 1.3.80 ) ff . Problem 1.3-12 : Backpropagation Derivation Using Matrix Calculus . Use a matrix calculus approach to derive the backpropagation ...
Introduction to Neural Networks with Java
... Backpropagation Algorithm • Implementing the Backpropagation Algorithm In this chapter we shall examine one of the most common neural network architec- tures, the feedforword backpropagation neural network. This neural network ...
Parallel Computing on Distributed Memory Multiprocessors
... Backpropagation is a supervised learning procedure for a class of artificial neural networks which has recently been widely used in training such neural networks to perform relatively nontrivial tasks like text - to - speech conversion ...
Computational Intelligence
... backpropagation [ 1 ] : • Backpropagation is a procedure for efficiently calculating the derivatives of some function of the outputs of any nonlinear differentiable system , with respect to all inputs and parameters of that system ...
IJCNN International Joint Conference on Neural Networks
... backpropagation algorithm is computationally expensive , we are interested in determining whether we can train by running normal backpropagation and then follow with a limited number of runs using double backpropagation . The results ...
Using Neural Networks for Risk Assessment in Internal Auditing
... backpropagation runs from each of. select various combinations of independent vari- ables . While a larger number of ... Backpropagation Nshellqual Appendix F Quantitative Backpropagation Nshellquant Appendix D Cleaned Qualitative ...
The Roots of Backpropagation
Now, for the first time, publication of the landmark work inbackpropagation!
Neural Networks and Fuzzy Systems
... backpropagation algorithm quickly dominated the neural - network litera- ture . Graduate students turned entrepreneurs and sold inexpensive backpropagation software . Others quietly tried to forecast stock prices . Hundreds of academic ...
Science of Artificial Neural Networks
... backpropagation learning algorithm , and also various modifications of the standard backpropagation algorithm ( e.g. , flat - spot elimination methods ) that have been discussed in the literature . Our simulation results suggest that ...
Parallel Implementations of Backpropagation Neural Networks on Transputers
This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers.
The 1994 IEEE International Conference on Neural Networks
... backpropagation algo- rithm is influenced . The backpropagation of a zero - error has to be performed before every ' real ' backpropagation pass as the offsets are dependent on the current state of the network , i.e. the output values ...
IEEE ... International Conference on Neural Networks
... backpropagation algorithm . Some of them are as follows . Experiment 1 The xor problem Backpropagation algorithm ( 5 ) , [ 10 ] , ( 11 ) ( 1 input layer , 1 hidden layer , 1 output layer ) Learning time = 1 hour Modified ...
1993 IEEE International Conference on Neural Networks, San Francisco, California, March 28-April 1, 1993
... backpropagation algorithm are usually trained either on slow serial computers with floating- point computation , or on dedicated digital hardware , which requires much area in silicon and may hamper backpropagation learning because of ...
Artificial Neural Networks, 2
... backpropagation algorithm : the left column reports the results obtained with the proposed algorithm while the right column reports the results obtained with the backpropagation algorithm , starting from the same initial point . For ...
Neural Networks in Bioprocessing and Chemical Engineering
... backpropagation experimental DiMassio et al ( 1991 ) alcoholic backpropagation industrial filter dynamics includedb Glassey et al . ( 1992 ) alcoholic backpropagation simulated filter dynamics included Karim and Rivera alcoholic ...
Optics in Atmospheric Propagation and Adaptive Systems
... backpropagation algorithm to imaging extended objects imbedded in turbid media such as clouds . Although the backpropagation algorithm was developed initially for biomedical applications , the underlying development is general enough to ...
Proceedings
... Backpropagation model 2 3 7 15 23 52 159 Kohonen SOM model 4 26 50 159 Clustering model using ABC1 7 19 41 108 181 247 500 1479 347 1085 11 15 20 Model Backpropagation model 9/11 11/11 11/11 Hybrid metric model using ABC1 6/11 9/11 10 ...