Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology

The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative memory (AM) and pattern recognition (PR) characteristics. Variants of the AdNN have also been used to obtain other PR phenomena, and even blur...

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Vydáno v:IEEE transactions on neural networks Ročník 20; číslo 11; s. 1797 - 1809
Hlavní autoři: Ke Qin, Oommen, B.J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.11.2009
Institute of Electrical and Electronics Engineers
Témata:
Adachi neural network (AdNN)
Algorithms
Applied sciences
Artificial Intelligence
Associative memory
Cellular neural networks
Chaos
Chaos theory
chaotic neural networks (CNNs)
chaotic pattern recognition (PR)
Computation
Computer networks
Computer science
Computer science; control theory; systems
Computer Simulation
Connectionism. Neural networks
Exact sciences and technology
Graphs
Linear Models
Mathematical analysis
Mathematical Computing
Network topology
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Pattern recognition
Pattern Recognition, Automated - methods
Time Factors
Tree graphs
Trees
Chaos
Input output
Gradient
blurring image
chaotic neural networks (CNNs)
Connected graph
Linear time
Pattern recognition
Neural network
Topology
chaotic pattern recognition (PR)
Weighted graph
Search algorithm
Adachi neural network (AdNN)
Associative memory
Spanning tree
ISSN:1045-9227, 1941-0093, 1941-0093
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Shrnutí:The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative memory (AM) and pattern recognition (PR) characteristics. Variants of the AdNN have also been used to obtain other PR phenomena, and even blurring. An unsurmountable problem associated with the AdNN and the variants referred to above is that all of them require a quadratic number of computations. This is essentially because the NNs in each case are completely connected graphs. In this paper, we consider how the computations can be significantly reduced by merely using a linear number of computations. To achieves this, we extract from the original completely connected graph one of its spanning trees. We then address the problem of computing the weights for this spanning tree. This is done in such a manner that the modified tree-based NN has approximately the same input-output characteristics, and thus the new weights are themselves calculated using a gradient-based algorithm. By a detailed experimental analysis, we show that the new linear-time AdNN-like network possesses chaotic and PR properties for different settings. As far as we know, such a tree-based AdNN has not been reported, and the results given here are novel.
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ISSN:1045-9227
1941-0093
1941-0093
DOI:10.1109/TNN.2009.2030582