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Titlebook: Hardware Annealing in Analog VLSI Neurocomputing; Bang W. Lee,Bing J. Sheu Book 1991 Springer Science+Business Media New York 1991 EEPROM.

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書目名稱Hardware Annealing in Analog VLSI Neurocomputing
編輯Bang W. Lee,Bing J. Sheu
視頻videohttp://file.papertrans.cn/425/424179/424179.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Hardware Annealing in Analog VLSI Neurocomputing;  Bang W. Lee,Bing J. Sheu Book 1991 Springer Science+Business Media New York 1991 EEPROM.
描述Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica- tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro- grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or
出版日期Book 1991
關(guān)鍵詞EEPROM; EPROM; Hardware; PROM; Signal; VLSI; analog; circuit; communication; computer; data compression; logic;
版次1
doihttps://doi.org/10.1007/978-1-4615-3984-1
isbn_softcover978-1-4613-6780-2
isbn_ebook978-1-4615-3984-1Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 1991
The information of publication is updating

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Hardware Annealing Theory,s to use gradient descent, which finds a direction for the next iteration from the gradient of the objective function. For complicated problems, the gradient descent technique often gets stuck at a local minimum where the objective function has surrounding barriers. In addition, the complexity of mo
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Bang W. Lee,Bing J. Sheue. The impact of PHA extraction may represent more than 50% of total production process. To ensure proper removal of the bioplastic from the bacterial cells several unitary operations must be performed in the extraction process. In addition, one also need to take in to consideration: reduction of pr
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