Reinforcement learning algorithm with network extension for pulse neural network

Koichiro Takita; Yuko Osana; Masafumi Hagiwara

Reinforcement learning algorithm with network extension for pulse neural network

Koichiro Takita, Yuko Osana, Masafumi Hagiwara

Department of Information and Computer Science

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.

Original language	English
Pages (from-to)	2586-2591
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	4
Publication status	Published - 2000 Dec 1
Event	2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA Duration: 2000 Oct 8 → 2000 Oct 11

ASJC Scopus subject areas

Control and Systems Engineering
Hardware and Architecture

Cite this

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title = "Reinforcement learning algorithm with network extension for pulse neural network",

abstract = "In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.",

author = "Koichiro Takita and Yuko Osana and Masafumi Hagiwara",

year = "2000",

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language = "English",

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journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",

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note = "2000 IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 08-10-2000 Through 11-10-2000",

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T1 - Reinforcement learning algorithm with network extension for pulse neural network

AU - Takita, Koichiro

AU - Osana, Yuko

AU - Hagiwara, Masafumi

PY - 2000/12/1

Y1 - 2000/12/1

N2 - In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.

AB - In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.

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M3 - Conference article

AN - SCOPUS:0034511471

SN - 0884-3627

VL - 4

SP - 2586

EP - 2591

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

T2 - 2000 IEEE International Conference on Systems, Man and Cybernetics

Y2 - 8 October 2000 through 11 October 2000

ER -

Reinforcement learning algorithm with network extension for pulse neural network

Abstract

ASJC Scopus subject areas

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