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bp_innerloop.m
Inner loop of the backpropagtion learning algorithm.
One hidden layer. Uses tanh as the transfer function.
Uses the following global variables for input and/or output:
Inputs1 - input patterns
Desired - desired output patterns
LearnRate - learning rate parameter
Momentum - momentum parameter
DerivIncr - increment to the derivative of the transfer function
(Fahlman s trick typical value 0.2)
Weights1 - first weight layer (updated by this routine)
Weights2 - second weight layer (updated by this routine)
deltaW1 - initialize to 0 before first call
deltaW2 - initialize to 0 before first call
TSS - total sum-squared error (set by this routine)
Recurrent state- bp_innerloop.m
Inner loop of the backpropagtion learning algorithm.
One hidden layer. Uses tanh as the transfer function.
Uses the following global variables for input and/or output:
Inputs1 - input patterns
Desired - desired output patterns
LearnRate - learning rate parameter
Momentum - momentum parameter
DerivIncr - increment to the derivative of the transfer function
(Fahlman s trick typical value 0.2)
Weights1 - first weight layer (updated by this routine)
Weights2 - second weight layer (updated by this routine)
deltaW1 - initialize to 0 before first call
deltaW2 - initialize to 0 before first call
TSS - total sum-squared error (set by this routine)
Recurrent state
Inner loop of the backpropagtion learning algorithm.
One hidden layer. Uses tanh as the transfer function.
Uses the following global variables for input and/or output:
Inputs1 - input patterns
Desired - desired output patterns
LearnRate - learning rate parameter
Momentum - momentum parameter
DerivIncr - increment to the derivative of the transfer function
(Fahlman s trick typical value 0.2)
Weights1 - first weight layer (updated by this routine)
Weights2 - second weight layer (updated by this routine)
deltaW1 - initialize to 0 before first call
deltaW2 - initialize to 0 before first call
TSS - total sum-squared error (set by this routine)
Recurrent state- bp_innerloop.m
Inner loop of the backpropagtion learning algorithm.
One hidden layer. Uses tanh as the transfer function.
Uses the following global variables for input and/or output:
Inputs1 - input patterns
Desired - desired output patterns
LearnRate - learning rate parameter
Momentum - momentum parameter
DerivIncr - increment to the derivative of the transfer function
(Fahlman s trick typical value 0.2)
Weights1 - first weight layer (updated by this routine)
Weights2 - second weight layer (updated by this routine)
deltaW1 - initialize to 0 before first call
deltaW2 - initialize to 0 before first call
TSS - total sum-squared error (set by this routine)
Recurrent state
(系统自动生成,下载前可以参看下载内容)
下载文件列表
elman_bp_innerloop.m
elman_bp_innerloop.m~
elman_test.m
elman_test.m~
elman_train.m
elman_train.m~
forwprop.m