**Complex-Valued
Neural Networks with Multi-Valued Neurons**

**2013 International Joint
Conference on Neural Networks (IJCNN-2013)**

**Dallas, Texas, USA, August 4, 2013, 8:00am**

**IJCNN-2013
Tutorial by**** **

**Igor
Aizenberg**

**Texas A&M University-Texarkana, USA**

**e-mail****: **igor.aizenberg at
tamut.edu URL: http://www.eagle.tamut.edu/faculty/igor

**Scope**

The **Complex-Valued Neural Networks (CVNNs)** is a quickly growing area that attracts more
and more researchers. There is a line of the CVNN Special Sessions organized
during last years, for example, at ICONIP 2002,
Singapore, ICANN/ICONIP
2003, Istanbul, ICONIP 2004, Calcutta, WCCI-IJCNN 2006, Vancouver, "Fuzzy Days 2006",
Dortmund, ICANN 2007, Porto, WCCI-IJCNN
2008, Hong Kong, IJCNN 2009, Atlanta, WCCI-IJCNN 2010, Barcelona, IJCNN
2011, San Jose, WCCI-IJCNN 2012, Brisbane. Everywhere
these sessions had large audience, which is growing continuously. There were
many interesting presentations and very productive discussions.

Due to the
computational and theoretical advantages that processing in the complex domain
offers over the real-valued domain, the area of complex-valued neural networks
is one of fastest growing research areas in the neural network community. In
addition, recent progress in pattern recognition, robotics, mathematical
biosciences, brain-computer interface design has
brought to light problems where nonlinearity, multidimensional data natures,
uncertainty, and complexity play major roles – complex-valued neural networks
are a natural model to account for these applications.

The most important
notion underlying the theory of complex-valued neural networks is that of the **phase information**. This enables us to
employ advanced concepts, such as phase synchrony and coherence, and to model
simultaneously the amplitude-phase relationships for a range of computational
scenarios.

The **Multi-Valued
Neuron (MVN)** is a complex-valued neuron with the inputs and output
located on the unit circle. MVN has a *circular
activation function*, which depends only on phase and projects a weighted
sum onto the unit circle. These specific properties determine many unique
advantages of MVN. The most important of them are the ability of MVN to learn
non-linearly separable input/output mappings without any network and simplicity
of derivative-free learning, which is based on the error-correction rule. For example, **such classical
non-linearly separable problems as XOR and Parity ***n*** are the simplest problems, which can be easily
learned by a single MVN with a periodic activation function, without any
network**.

MVN-based
complex-valued neural networks also have a number of unique advantages.

For example, the **Multilayer
Feedforward Neural Network with Multi-Valued Neurons (MLMVN)**
significantly outperforms a classical multilayer perceptron (MLP) and many kernel-based techniques in terms of
generalization capability and the number of parameters employed. MLMVN learns
significantly faster than MLP, and MLMVN's learning algorithm, as well as the error
backpropagation rule, is derivative-free. The MLMVN learning algorithm is based
on the same error-correction learning rule as the MVN learning algorithm. **MVN-based Hopfield
neural networks** have shown unique
capabilities as **associative memories**.

Complex-valued neural
network models have been shown not only to exhibit enhanced accuracy, but also
to facilitate physical interpretation of their variables. One of the recent
applications of MLMVN is its use for **decoding
of signals in EEG-based brain-computer interfaces**.

The circularity of
the MVN activation function is natural and very suitable in applications where
the processed and analyzed signals are represented in the **frequency domain**. It can be especially interesting to use MVN in
studies devoted to modeling and simulations of biological neurons.

The synergy of
complex nonlinearity, circularity, the ability to separate linearly those
mappings, which are not separable in the real domain, underpins this tutorial,
which aims at providing a rigorous unifying framework for the design, analysis,
and interpretation of complex neural network models. The material is supported
by detailed case studies across learning, pattern recognition, image processing,
mathematical biosciences, and computational neuroscience, highlighting the
practical usefulness of MVN-based complex-valued neural networks.

This tutorial represents a quantum step forward from the presenter's
earlier tutorials given together with Prof. Danilo mandic and Prof. Akira Hirose at IJCNN-2010 and IJCNN-2011. It
provides a complete and rigorous overview of state-of-the-art in the area,
supported by practical applications and working solutions.

The presenter has a recent
fundamental research monograph in the area (published by Springer in 2011)
focusing on neural networks with multi-valued neurons and their applications in
pattern recognition and classification. The presentation will be based on this
monograph and recent journal publications followed it. The material will be
explicitly illustrated and a number of practical applications in pattern
recognition, classification, intelligent image processing and time series
prediction will be considered in detail.

**Contents of the Tutorial**

- Brief
introduction. Complex-valued neural networks: why we need them?
- Multiple-valued
(
*k*-valued) logic over the field of complex numbers.*k*-separability of*n*-dimensional space.

A multi-valued neuron (MVN) and its functionality. Discrete and continuous MVN. - Learning
rules for MVN. The Hebbian rule. The "closeness" rule. The
error-correction rule. MVN learning algorithm and its convergence.

Choice of the best starting weights for the learning process. - MVN
with a periodic activation function (MVN-P) and solving non-linearly
separable problems using a single MVN-P

(XOR, parity*n*, mod*k*addition of*n*inputs, various benchmark problems). - A
multilayer feedforward neural network based on multi-valued neurons
(MLMVN). The error backpropagation and its specific organization for the
MLMVN.

The error-correction learning rule for MLMVN. - A
derivative-free learning MLMVN learning algorithm based on the
error-correction learning rule and its convergence. Hard Margins learning
and soft margins learning for MLMVN.
- Solving
the popular benchmark classification and prediction problems and
comparison with the competitive solutions (standard backpropagation
network, kernel-based networks, SVM, neuro-fuzzy networks).
- Application
of MLMVN for solving real-world problems: blur and blur parameters
identification for image deblurring; recognition of blurred images;
intelligent edge detection; detection of impulse noise; time-series
prediction; classification of microarray gene expression data. Frequency
domain as a natural source of the features for the classification
purposes.
- MLMVN
as a signal decoder in an EEG-based brain-computer interface. Similarity
of MVN and biological neurons.
- MVN-based
associative memories and their applications.

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