Evaluation of the analogical feature input in hardware perceptrons

• Main Authors: Kun-Yan Jiang, 江昆諺
• Other Authors: Syang-Ywan Jeng
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/a4sz7h

碩士 === 中原大學 === 電子工程研究所 === 103 === Artificial neural network has been applied extensively in audio processing and pattern recognition, such as information security, authentication, medical image processing, intelligent electronic products, etc. We implement the 4x3 NOI neural arrays by using 0.25um CMOS process, with the perceptron algorithms for analogical pattern recognition. Analogical features were used as multi-value inputs which were different from digitized feature inputs. In order to study the learning performance of hardware system, the MATLAB software is applied for simulating the training trend with different input pattern features. Moreover, we also simulate two kind of different weight updatings by software to evaluate system’s efficiency. These methods include whole Chip Erase and Single Neuron Erase. Besides, the optimization parameter of the system judgment, input voltage range and coverage of pattern are also discussed. Finally, the simulation results through hardware training are also verified. From the results in our hardware training, we found that using the analogical feature can speed up the system learning, and the learning efficiency increase 25 times. Furthermore, in the noise testing, the recognition rate of system also improves 15.85%. In summary, it has been found that analogical features improve the system not only the training speed but also the recognition rate.