Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science Technology, Rolla, MO 65409, USA.
Neural Netw. 2010 Jun;23(5):583-6. doi: 10.1016/j.neunet.2009.12.009. Epub 2010 Jan 2.
Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach.
训练单个同时递归神经网络 (SRN) 以学习多输入多输出 (MIMO) 系统的所有输出是一个难题。本文提出了一种新的训练算法，该算法结合了群体智能和量子原理的概念。该训练算法称为量子注入粒子群优化 (PSO-QI)。为了提高学习的有效性，在训练中引入了两步学习方法。第一步的学习目标是在考虑所有输出误差的情况下找到 SRN 中最优的权重集。在第二步中，目标是通过微调各自的 SRN 输出权重来最大化每个输出动态的学习。为了演示 PSO-QI 训练算法和两步学习方法的有效性，本文提出了两个 SRN 学习 MIMO 系统的例子。第一个例子是学习基准 MIMO 系统，第二个例子是设计多机电力系统的广域监测系统。结果表明，当使用 PSO-QI 算法和两步学习方法训练时，SRN 可以有效地学习 MIMO 系统。
