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| 输入输出均为时变函数的过程神经网络及应用 |
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何新贵1, 许少华2
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1.北京大学,信息科学技术学院,视觉与听觉信息处理国家重点实验室,北京,100871;2.北京航空航天大学,计算机科学与工程系,北京,100083
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| 摘要: |
| 为了解决实际系统中输入、输出经常是时变连续函数的问题,提出了一类基于基函数展开的过程神经元网络模型.该模型利用过程神经元网络所具有的对时间变量的非线性映射能力,实现系统的输入、输出之间的连续映射关系.另外,还给出了一种学习算法.为了简化计算,选择正交函数作为基函数,并以油藏开发仿真为例,验证了模型和算法的有效性. |
| 关键词: 过程神经元网络 函数逼近 时变函数 正交基函数 学习算法 |
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| 基金项目:Supported by the Natural Science Foundation of Heilongjiang Province of China under Grant No.F01-20 (黑龙江省自然科学基金) |
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| Process Neural Network with Time-Varied Input and Output Functions and Its Applications |
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HE Xin-Gui,XU Shao-Hua
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| Abstract: |
| In order to solve the problems in real systems where inputs and outputs are time-varied continuous functions, a process neural network model based on expansion of base functions is proposed in this paper. In this model, the continuous input-output mapping of the system is realized by nonlinear mapping capability to the time variable of process neural networks. A learning algorithm is also given in this paper. In order to simplify the algorithm, orthogonal functions are selected as base functions, and the effectiveness of the model and the algorithm is proved by simulation of oil reservoir exploitation. |
| Key words: process neural network approximation of function time-varied function orthogonal base function learning algorithm |
