000			03593nam a22004455i 4500
001			978-3-642-20353-4
003			DE-He213
005			20140220083800.0
007			cr nn 008mamaa
008			110713s2011 gw | s |||| 0|eng d
020			_a9783642203534 _9978-3-642-20353-4
024	7		_a10.1007/978-3-642-20353-4 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aAizenberg, Igor. _eauthor.
245	1	0	_aComplex-Valued Neural Networks with Multi-Valued Neurons _h[electronic resource] / _cby Igor Aizenberg.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVI, 264p. 258 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v353
505	0		_aWhy We Need Complex-Valued Neural Networks? -- The Multi-Valued Neuron -- MVN Learning -- Multilayer Feedforward Neural Network based on Multi-Valued Neurons (MLMVN) -- Multi-Valued Neuron with a Periodic Activation Function -- Applications of MVN and MLMVN.
520			_aComplex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories.   The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642203527
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v353
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-20353-4
912			_aZDB-2-ENG
999			_c107770 _d107770