Module NeuroController

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Example controller which uses a prelearned neural network for controlling the inverted pendulum.

Classes

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NeuroController
Neural net controller.

Functions

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transpose(matrix)
transpose a matrix

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vectormatrixmultiply(a, b)
multiply vector with matrix.

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matrixmatrixmultiply(a, b)
multiply two matrices.

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applyfuncvector(f, vector)
apply function to all elements of vector

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applyfuncmatrix(f, matrix)
apply function to all elements of matrix

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prepareWeightMatrices(weights)
build array and transpose

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makeVector(data)
build vector from list

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sigmoid(x)
sigmoidal function.

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feedForwardStep(data, weights)
Feed forward data from one layer to the next through the weight matrix.

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Variables

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input_hidden1 = prepareWeightMatrices(([0, 0, 0, 0, 0], [1.352...
weights from input layer to hidden layer 1

hidden1_hidden2 = prepareWeightMatrices(([0, 0, 0, 0, 0, 0, 0,...
weights from hidden layer 1 to hidden layer 2

hidden2_output = prepareWeightMatrices(([2.6952314,-2.1971807,...
weights from hidden layer 2 to output layer 2

arctanh_0_8 = 1.098612
scaling factor for in-/outputs

Function Details

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vectormatrixmultiply(a, b)

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multiply vector with matrix. b must be transposed

matrixmatrixmultiply(a, b)

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multiply two matrices. b must be transposed

Variables Details

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input_hidden1

weights from input layer to hidden layer 1

Value:

prepareWeightMatrices(([0, 0, 0, 0, 0], [1.3523004,-67.2762222,-2.7101
681,-1.2252374,-0.1924101], [3.5358984,-3.0979257, 11.8105240,-2.04001
95,-5.6440673], [2.2998035,-6.4394584,-0.4456880, 13.2240639,-1.941612
4], [-5.3507094,-25.7565060,-0.8581331,-2.4438772, 0.3309670], [-11.85
40325,-27.4872322,-0.4254195, 2.7986245, 0.8198755], [-2.3312118,-0.40
25910,-2.0389884, 3.8539076, 3.2488785], [-14.8030014,-19.3887348,-2.1
227808,-2.5157831,-1.3637272], [-21.9581738,-38.1970100,-0.7618715, 2.
2844830, 1.3290895]))

hidden1_hidden2

weights from hidden layer 1 to hidden layer 2

Value:

prepareWeightMatrices(([0, 0, 0, 0, 0, 0, 0, 0, 0], [-0.2244532, 6.822
6333, 5.7899127, 1.6202815, 8.7983570,-15.6285400, 3.5351822,-8.275008
2,-6.9833350], [-14.1263733,-9.2765779, 12.6322460, 0.0562423,-7.27849
58,-4.2336187, 6.9077992, 3.8148420,-4.8503165], [-1.7320144,-2.200586
8,-0.0205769, 2.0873408, 3.7128136,-3.4949446, 2.3434448, 7.4144168,-9
.0030613], [-13.3777761, 14.2169991, 6.5337262,-1.3075531,-8.6651535,-
24.3653679, 2.9219866,-10.9699783,-7.3186345]))

hidden2_output

weights from hidden layer 2 to output layer 2

Value:

prepareWeightMatrices(([2.6952314,-2.1971807,-1.7768925,-1.1596987,-3.
1659257],))