The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 05, 1994
Filed:
Dec. 11, 1992
Toshiyuki Furuta, Yokohama, JP;
Hiroyuki Horiguchi, Yokohama, JP;
Hirotoshi Eguchi, Yokohama, JP;
Yutaka Ebi, Yokohama, JP;
Tatsuya Furukawa, Yokohama, JP;
Yoshio Watanabe, Kawasaki, JP;
Toshihiro Tsukagoshi, Itami, JP;
Takahiro Watanabe, Sagamihara, JP;
Shuji Motomura, Yokohama, JP;
Atsuo Hashimoto, Nishinomiya, JP;
Sugitaka Oteki, Minoo, JP;
Satoshi Otsuki, Nishinomiya, JP;
Eiki Aono, Minoo, JP;
Takashi Kitaguchi, Yokohama, JP;
Ricoh Company, Ltd., Tokyo, JP;
Abstract
A neuron unit processes a plurality of input signals and for outputs an output signal which is indicative of a result of the processing, and includes input lines for receiving the input signals, a forward process part including a supplying part for supplying weight functions and an operation part for carrying out an operation on each of the input signals using one of the weight functions and for outputting the output signal, and a self-learning part including a function generating part for generating new weight functions based on errors between the output signal of the forward process part and teaching signals and a varying part for varying the weight functions supplied by the supplying part of the forward process part to the new weight functions generated by the generating part. The supplying part includes a memory for storing each weight function in the form of a binary value, and a generating circuit for generating a random pulse train based on each binary value stored in the memory. The random pulse train describes each weight function in the form of a pulse signal having a pulse density.