An execution system of logic programming language using neural networks-an improvement of the transformation algorithm

Kikuchi, Yukihiro; Murakoshi, H.; Funakubo, Noboru

doi:10.1109/iecon.1998.723941

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the conversion of such a program to the neural network, the list structure is decomposed and the decomposed entity is handled as an independent entity, so that the above conversion algorithm can be applied (an example is shown in Fig. 4 below) [13,14]. When the program structure permits infinite recursive calls, an infinite number of cells are required if the method is directly applied, making the conversion impossible.…”

Section: Basic Conversionmentioning

confidence: 99%

A neural network approach to interference mechanism for logic programming language

Takeda¹,

Kawahara²,

Kikuchi³

et al. 2004

Systems & Computers in Japan

View full text Add to dashboard Cite

SUMMARYIt is known that the Hopfield-type neural network can solve the combinatorial optimization problem with a high speed. In order to realize that advantage, however, the energy function must be constructed for each problem, and there is no general framework to be applied to various problems. This paper notes that the logic programming language Prolog can be represented as the logical operation of facts and the rules, and proposes an algorithm, which converts the logical operation network to the Hopfield-type neural network. Using this conversion algorithm, the power of the Hopfield-type network as an optimization machine can be utilized in the logic inference described by Prolog. Then, it is expected that the range of its application will be enlarged greatly.

show abstract

Section: Basic Conversionmentioning

confidence: 99%