A construction of neural-net based AI systems

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http://doi.ieeecomputersociety.org/10.1109/ICECCS.1995.479369

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	ASCII Text	x
	M. Kawada, Xu Wu, T. Ae, "A construction of neural-net based AI systems," Engineering of Complex Computer Systems, IEEE International Conference on, pp. 424, First IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'95), 1995.

	BibTex	x
	@article{ 10.1109/ICECCS.1995.479369, author = {M. Kawada and Xu Wu and T. Ae}, title = {A construction of neural-net based AI systems}, journal ={Engineering of Complex Computer Systems, IEEE International Conference on}, volume = {0}, year = {1995}, isbn = {0-8186-7123-8}, pages = {424}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICECCS.1995.479369}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Engineering of Complex Computer Systems, IEEE International Conference on TI - A construction of neural-net based AI systems SN - 0-8186-7123-8 SP EP A1 - M. Kawada, A1 - Xu Wu, A1 - T. Ae, PY - 1995 KW - neural net architecture; real-time systems; case-based reasoning; pattern classification; learning (artificial intelligence); knowledge based systems; algorithm theory; neural-net based AI system construction; neural network technique; real-time neuron-based AI architecture; NAI; real-time case-based reasoning system; winner-take-all type neural network; real-time classification; real-time retrieval; massive case-base; flexible learning; learning algorithms; supervised learning; unsupervised learning; self-organizing learning algorithm; LVQ1 learning algorithm VL - 0 JA - Engineering of Complex Computer Systems, IEEE International Conference on ER -

M. Kawada, Dept. of Electron. Eng., Hiroshima Univ., Japan

Xu Wu, Dept. of Electron. Eng., Hiroshima Univ., Japan

T. Ae, Dept. of Electron. Eng., Hiroshima Univ., Japan

The paper investigates the integration of the neural network technique and traditional AI techniques towards the realization of a real-time neuron-based AI architecture. As the first step of our project, we propose a neural network based AI system, called NAI. NAI is a kind of real-time CBR (case-based reasoning) system in which the WTA (winner-take-all) type neural network is embedded for supporting the real-time classification and retrieval of a massive case-base. For flexible learning with the WTA neural network, two learning algorithms (supervised and unsupervised) have been developed on the basis of the LVQ1 and self-organizing learning algorithms.

Index Terms:

neural net architecture; real-time systems; case-based reasoning; pattern classification; learning (artificial intelligence); knowledge based systems; algorithm theory; neural-net based AI system construction; neural network technique; real-time neuron-based AI architecture; NAI; real-time case-based reasoning system; winner-take-all type neural network; real-time classification; real-time retrieval; massive case-base; flexible learning; learning algorithms; supervised learning; unsupervised learning; self-organizing learning algorithm; LVQ1 learning algorithm

Citation:

M. Kawada, Xu Wu, T. Ae, "A construction of neural-net based AI systems," iceccs, pp.424, First IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'95), 1995

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