CAJ | 학술논문

通过标准自适应共振理论神经网络(Adaptive Resonance Theory, ART),设计和实现了一个字符识别器,针对标准的 ART1网络存在的不足,即网络的学习不稳定,对样本输入顺序比较敏感等问题,给出了改进方法,用C语言实现了这2种字符识别器,实验结果表明这2种字符识别器能够对不同的字符进行识别,改进方法比基于标准ART1网络具有更好的稳定性。
통과표준자괄응공진이론신경망락(Adaptive Resonance Theory, ART),설계화실현료일개자부식별기,침대표준적 ART1망락존재적불족,즉망락적학습불은정,대양본수입순서비교민감등문제,급출료개진방법,용C어언실현료저2충자부식별기,실험결과표명저2충자부식별기능구대불동적자부진행식별,개진방법비기우표준ART1망락구유경호적은정성。
Adaptive Resonance Theory (ART) neural network is analyzed in this paper. A character recognizer is designed and implemented based on the standard ART1 network, aiming at the shortcomings of the standard ART1 network, which concludes the unstablity of network learning and the over sensitiveness to the input sample sequence. This paper gives a method to improve the implementation in C 2 kind of identifier. Experimental validation of these two character recognizer can identify the different character. The improvement method based on standard ART1 network has better stability.