Using Artificial Neural Networks to Model Nonlinearity
研究用多层感知器和径向基函数神经网络建模工作满意度与工作绩效之间的非线性关系，提供分析框架并发现普遍的非线性模式。
Neural networks are advanced pattern recognition algorithms capable of extracting complex, nonlinear relationships among variables. This study examines those capabilities by modeling nonlinearities in the job satisfaction—job performance relationship with multilayer perceptron and radial basis function neural networks. A framework for studying nonlinear relationships with neural networks is offered. It is implemented using the job satisfaction—job performance relationship with results indicative of pervasive patterns of nonlinearity.