计算机科学
人工神经网络
乙状窦函数
激活函数
概率神经网络
概率逻辑
人工智能
反向传播
计算
贝叶斯定理
可靠性(半导体)
时滞神经网络
非线性系统
功能(生物学)
随机神经网络
机器学习
贝叶斯概率
算法
功率(物理)
物理
量子力学
进化生物学
生物
标识
DOI:10.1016/0893-6080(90)90049-q
摘要
By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network (PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware “neurons” that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation.
科研通智能强力驱动
Strongly Powered by AbleSci AI