An approach to compensating sensor drift based on recurrent neural networks is discussed in the paper.

Recurrent Neural Network-based Identification Modeling Study

基于循环神经网络的辨识建模研究

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An RNN-Based Approach to Compensating Sensor Drift

基于循环神经网络的传感器漂移补偿方法

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Speech Recognition Model Based on Recurrent Neural Networks

基于循环神经网络的语音识别模型

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An evolutionary algorithm of constructs recurrent neural Networks

一种训练循环神经网络的演化算法

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Multisense Information mixing System Based on Round Nerve Network

基于循环神经网络的多传感信息融合系统的应用

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Recurrent Neural Network Modeling and Its Application in Nonlinear Predictive Control

循环神经网络建模在非线性预测控制中的应用

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W-CDMA Systems Power Control Based on Circular Neural Networks

基于循环神经网络的宽带CDMA系统的功率控制

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Aircraft target recognition based on radar range profile sequences using recurrent neural network

基于雷达距离象序列的循环神经网络飞机目标识别

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Identification of nonlinear based on high order diagnoal recurrent neural network

高阶对称循环神经网络的非线性辨识

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Q-learning algorithm in the reinforcement learning needs a lot of storage space while solving the more complex problems.

而强化学习中的Q学习算法在解决较为复杂问题时，需要大量的存储空间，忆阻交叉阵列恰好能够解决这个问题。

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Q learning algorithm is the most popular reinforcement learning algorithm, but the algorithm exist some problems.

目前主流的强化学习算法是Q学习算法，但Q学习本身存在一些问题。

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An Efficient Solution Algorithm Based on Reinforcement Learning for Some NP Problems

基于强化学习的一类NP问题求解算法

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Adaptive Fuzzy RBF Networks Learning for Autonomous Multi-robots

自适应模糊RBF神经网络的多智能体机器人强化学习

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Mobile Robot Local Path Planning Based on Q Reinforcement Learning and CMAC

基于Q强化学习与CMAC的移动机器人局部路径规划

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Reinforcement learning is the main kind of learning method, which is recognized as an ideal technology to construct the intelligent agent.

在Agent的学习中，强化学习是其中主要的一类学习方法，被公认为是构成Agent的核心技术之一。

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Research of Distributed Power Generation System Based on Multi-Agent Co-operation Strengthens Study

基于Multi-Agent协作强化学习的分布式发电系统的研究

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An algorithm of reinforcement learning for finite-horizon Markov decision processes

一种有限时段Markov决策过程的强化学习算法

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Fourthly, leaning agent adjusts the domain model and user model adaptively by reinforcement learning and genetic algorithm.

第四，作者研究了学习Agent使用强化学习、遗传算法自适应地调整领域模型和用户模型。

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Option Automatic Generation in Hierarchical Reinforcement Learning

分层强化学习中的Option自动生成算法

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A Novel Multi-Agent Reinforcement Learning Approach

一种新颖的多agent强化学习方法

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