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003			ES-MaUEC
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007			cr nn 008mamaa
008			160602s2016 gw | s |||| 0|eng d
020			_a9783319340876
040			_aES-MaUEC
050		4	_aQ325.78 _bG395 2016
082	0	4	_a006.3
100	1		_aGaxiola, Fernando. _999481 _0Local
245	1	0	_aNew Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks _cby Fernando Gaxiola, Patricia Melin, Fevrier Valdez
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (IX, 102 páginas) _b94 ilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aSpringerBriefs in Applied Sciences and Technology _x2191-530X
505	0		_aIntroduction.-Theory and Background -- Problem Statement an Development -- Simulations and Results -- Conclusions.
520	3		_aIn this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods. The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.
650		7	_aRedes neuronales artificiales _0comprobar BNE19900997218 _2embne _9678664
700	1		_aMelin, Patricia _d1962- _0Local _997926
700	1		_aValdez, Fevrier. _999482 _0Local
710	2		_aSpringerLink (Online service) _0Local _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-34087-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319340876
907			_a.b12953416 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aQ325.78 G395 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11595930 _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0