FROM DISCRETE-TIME MODELS TO CONTINUOUS-TIME, ASYNCHRONOUS MODELING OF FINANCIAL MARKETS

Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent-based modeling.     

典型人工神经网络的结构、功能及其在智能系统中的应用

人工神经网络已在各个领域得到广泛的应用, 尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优 化计算等方面更是得到人们的极大关注．本文从网络在智能系统中建模及控制器设计的具体 训练结构入手,详细介绍了BP网络在系统控制中的典型应用方式,并根据不同网络所具有的 功能,从性能对比的角度对人工神经网络在上述各方面的应用给予综述．