Function plays an important role in mathematics and many science branches. As the fast development of computer technology, more and more study on computational function analysis, e.g., Fast Fourier Transform, Wavelet Transform, Curve Function, are presented in these years. However, there are two main problems in these approaches: 1) hard to handle the complex functions of stationary and non-stationary, periodic and non-periodic, high order and low order; 2) hard to generalize the fitting functions from training data to test data. In this paper, a multiple regression based function fitting network that solves the two main problems is introduced as a predicable function fitting technique. This technique constructs the network includes three main parts: 1) the stationary transform layer, 2) the feature encoding layers, and 3) the fine tuning regression layer. The stationary transform layer recognizes the order of input function data, and transforms non-stationary function to stationary function. The feature encoding layers encode the raw input sequential data to a novel linear regression feature that can capture both the structural and the temporal characters of the sequential data. The fine tuning regression layer then fits the features to the target ahead values. The fitting network with the linear regression feature layers and a non-linear regression layer come up with high quality fitting results and generalizable predictions. The experiments of both mathematic function examples and the real word function examples verifies the efficiency of the proposed technique. Comment: 14 pages, 3 figures
2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) Industrial and Commercial Power System Asia (I&CPS Asia), 2020 IEEE/IAS. :1745-1750 Jul, 2020
Wang, Yingxu, Pedrycz, Witold, Baciu, George, Chen, Ping, de Garis, Hugo, Shi, Zhongzhi, Wang, Guoyin, Wang, Patrick, and Yao, Yiyu
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on. :8-16 Jul, 2010
2010 10th IEEE International Conference on Computer and Information Technology Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on. :203-209 Jun, 2010
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Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Evolutionary computation Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on. 1:477-482 vol.1 2002
Du, Wei, Ding, Shifei, Zhang, Chenglong, and Shi, Zhongzhi
IEEE Transactions on Neural Networks and Learning Systems; October 2023, Vol. 34 Issue: 10 p6851-6860, 10p
Abstract
Most recent research on multiagent reinforcement learning (MARL) has explored how to deploy cooperative policies for homogeneous agents. However, realistic multiagent environments may contain heterogeneous agents that have different attributes or tasks. The heterogeneity of the agents and the diversity of relationships cause the learning of policy excessively tough. To tackle this difficulty, we present a novel method that employs a heterogeneous graph attention network to model the relationships between heterogeneous agents. The proposed method can generate an integrated feature representation for each agent by hierarchically aggregating latent feature information of neighbor agents, with the importance of the agent level and the relationship level being entirely considered. The method is agnostic to specific MARL methods and can be flexibly integrated with diverse value decomposition methods. We conduct experiments in predator–prey and StarCraft Multiagent Challenge (SMAC) environments, and the empirical results demonstrate that the performance of our method is superior to existing methods in several heterogeneous scenarios.
