基于大脑神经元放电的脑-机接口技术

基于大脑运动皮层神经元放电的脑-机接口通过记录大脑运动皮层神经元的放电信号控制瘫痪肢体或假肢运动,其软硬件核心为神经元群体解码和神经元放电活动的检测。解码方法分为推理算法和分类器方法,检测方法通过在大脑运动皮层区植入长效电极记录单个或群体神经元的放电活动。分析表明,脑-机接口技术应在更多脑区域上植入长效电极达到更好控制设备的目的,各类解码算法应通过联合并加入反馈信号提高对神经元信号的解码效果。     

基于精细神经元的类脑感知学习模型

大脑如何实现学习以及感知功能对于人工智能和神经科学领域均是一个重要问题.现有人工神经网络由于结构和计算机制与真实大脑相差较大,无法直接用于理解真实大脑学习以及处理感知任务的机理.树突神经元模型是一种对大脑神经元树突信息处理过程进行建模仿真的计算模型,相比人工神经网络更接近生物真实.使用树突神经网络模型处理学习感知任务对理解真实大脑的学习过程有重要作用.然而,现有基于树突神经元网络的学习模型大都局限于简化树突模型,无法完整建模树突的信号处理过程.针对这一问题,提出一种基于精细中型多棘神经元网络的学习模型,使得精细神经网络可以通过学习完成相应感知任务.实验表明,在经典的图像分类任务上,所提模型可以达到很好的分类性能.此外,精细神经网络对于噪声干扰有很强的鲁棒性.对网络特性进行进一步分析,发现学习后网络中的神经元表现出了刺激选择性这种神经科学中的经典现象,表明所提模型具有一定的生物可解释性,同时也表明刺激选择特性可能是大脑通过学习完成感知任务的一种重要特性.     

神经元实时编码的分类和预测模型研究

为了研究神经元放电的内在规律及解决传统线性分析方法不能对神经元采样数据进行有效分类的问题,提出了正则化线性判别分析法和最近收缩质心法.根据神经元数据自身的特点,设计了一个新的分析神经元放电频率的方法,并通过交叉验证比较了各算法的正确性.实验结果表明了提出的新方法的有效性,证明了神经元放电活动的内在规律性以及利用对神经元集群放电活动的分析对外界刺激分类和预测的可行性.     

基于量子计算多Agent的人工神经网络训练方法

人工神经网络是可用于建模和求解各种复杂非线性现象的工具.针对传统神经网络训练时间长、节点数目受计算机能力限制等缺点,提出了一种新的多Agent系统理论(MAS)和量子算法的人工神经网络.在人工神经网络训练方法中,每个神经元或节点是一个量子Agent,通过强化学习算法后具有学习能力,然后用QCMAS强化学习算法作为新的神经网络的学习规则.这种新的人工神经网络法具有很好的并行工作能力而且训练时间比经典算法短,实验结果证明了方法的有效性.     

基于多类最小二乘支持向量机的神经元信号识别

针对大脑运动皮层群体神经元信号与运动行为关系的分析,提出一种基于二叉树的最小二乘支持向量机多类分类算法.在对猴子进行三维空间中8个方向手臂运动实验记录的多通道神经元信号的分析中,通过与标准支持向量机和学习矢量量化神经网络的比较,说明该方法不仅与标准支持向量机同样具有比学习矢量量化方法更强的学习能力和预测能力,而且运算时间比标准支持向量机更短.比较结果表明最小二乘支持向量机对于神经元信号分析的有效性和优越性,进而有利于实现性能更高的用于神经康复的脑机接口系统.     

一种生长型神经网络的倒立摆控制方案

针对倒立摆系统,提出了在结构上可生长的神经网络控制方案.网络利用细胞生长结构算法,在工作域中实现对状态变量的模式分类,并通过新神经元的插入实现网络规模的生长演化.在输出域中针对倒立摆控制任务采用强化Hebb学习机制,实现不同的神经元以最佳方式响应不同性质的信号刺激.仿真表明,通过神经网络自身的发育,该方案有效控制了倒立摆系统.     

生物视觉系统的神经网络编码模型综述

生物视觉系统的研究一直是计算机视觉算法的重要灵感来源。有许多计算机视觉算法与生物视觉研究具有不同程度的对应关系,包括从纯粹的功能启发到用于解释生物观察的物理模型的方法。从视觉神经科学向计算机视觉界传达的经典观点是视觉皮层分层层次处理的结构。而人工神经网络设计的灵感来源正是视觉系统中的分层结构设计。深度神经网络在计算机视觉和机器学习等领域都占据主导地位。许多神经科学领域的学者也开始将深度神经网络应用在生物视觉系统的计算建模中。深度神经网络多层的结构设计加上误差的反向传播训练,使得它可以拟合绝大多数函数。因此,深度神经网络在学习视觉刺激与神经元响应的映射关系并取得目前性能最好的模型同时,网络内部的单元甚至学习出生物视觉系统子单元的表达。本文将从视网膜等初级视觉皮层和高级视觉皮层(如,视觉皮层第4区(visual area 4,V4)和下颞叶皮层(inferior temporal,IT))分别介绍基于神经网络的视觉系统编码模型。主要内容包括:1)有关视觉系统模型的概念与定义;2)初级视觉系统的神经网络预测模型;3)任务驱动的高级视觉皮层编码模型。最后本文还将介绍最新有关无监督学习的神经编码...     

基于ARM的免疫原理解耦算法的研究

为满足复杂工业过程控制对鲁棒性的要求,将采用免疫神经元PID控制解耦算法进行试验,验证改进算法的优化性能.该控制系统的硬件设计采用嵌入式系统(ARM)方式,用于环境试验箱进行试验,软件采用C语言的编程规则进行设计,解耦算法的实现采用了神经元控制结构和免疫反馈原理二者相结合.通过实验表明,这种新的控制解耦算法学习速度快,动态响应时间短,实现了参数的在线优化,提高了系统的鲁棒性.     

一种改进的皮层网络环境认知模型

前额皮层是哺乳动物环境认知能力的重要神经生理基础, 许多研究基于皮层网络结构对前额皮层进行计算建模, 使机器人能够完成环境认知与导航任务. 但是, 对皮层网络模型神经元噪声(一种干扰神经元规律放电的内部电信号)鲁棒性方面的研究不多, 传统模型采用的奖励扩散方法存在着导航性能随噪声增大而下降过快的问题, 同时其路径规划方法效果不好, 无法规划出全局最短路径. 针对上述问题, 本文在皮层网络的基础上引入波前传播算法, 结合全局抑制神经元来设计奖励传播回路, 同时将时间细胞和位置偏好细胞引入模型的路径规划回路以改善路径规划效果. 为了验证模型的有效性, 本文复现了心理学上两个经典的环境认知实验. 实验结果表明, 本模型与其他皮层网络模型相比表现出更强的神经元噪声鲁棒性. 同时, 模型保持了较好的路径规划效果, 与传统路径规划算法相比具有较高的效率.     

一类反馈过程神经元网络模型及其学校算法

提出了一种基于权函数基展开的反馈过程神经元网络模型．该模型为三层结构，由输入层、过程神经元隐层和过程神经元输出层组成．输入层完成系统时变过程信号的输入及隐层过程神经元输出信号向系统的反馈；过程神经元隐层用于完成输入信号的空间加权聚合和激励运算，同时将输出信号传输到输出层并加权反馈到输入层；输出层完成隐层输出信号的空间加权聚集和对时间的聚合运算以及系统输出．文中给出了学习算法，并以旋转机械故障自动诊断问题为例验证了模型和算法的有效性．     

多机器人动态编队的强化学习算法研究

在人工智能领域中，强化学习理论由于其自学习性和自适应性的优点而得到了广泛关注．随着分布式人工智能中多智能体理论的不断发展，分布式强化学习算法逐渐成为研究的重点．首先介绍了强化学习的研究状况，然后以多机器人动态编队为研究模型，阐述应用分布式强化学习实现多机器人行为控制的方法．应用SOM神经网络对状态空间进行自主划分，以加快学习速度；应用BP神经网络实现强化学习，以增强系统的泛化能力；并且采用内、外两个强化信号兼顾机器人的个体利益及整体利益．为了明确控制任务，系统使用黑板通信方式进行分层控制．最后由仿真实验证明该方法的有效性．     

GA-based fuzzy reinforcement learning for control of a magneticbearing system

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.     

Controlling chaos by GA-based reinforcement learning neural network

Proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfil the reinforcement learning task. Structurally, the TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the TDGAR learning system. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. This can usually accelerate the GA learning since an external reinforcement signal may only be available at a time long after a sequence of actions have occurred in the reinforcement learning problems. By defining a simple external reinforcement signal. the TDGAR learning system can learn to produce a series of small perturbations to convert chaotic oscillations of a chaotic system into desired regular ones with a periodic behavior. The proposed method is an adaptive search for the optimum control technique. Computer simulations on controlling two chaotic systems, i.e., the Henon map and the logistic map, have been conducted to illustrate the performance of the proposed method.     

Reinforcement learning to train a cooperative network with both discrete and continuous output neurons

We propose a reinforcement learning algorithm to train a cooperative network with both discrete and continuous output neurons based on the finding that discrete and continuous motorneurons coexist in the gill-withdrawal neural network of the sea mollusk, Aplysia. The network was trained to control an inverted pendulum. Simulation experiments showed that the two output neurons had distinct but cooperative roles: the discrete output neuron was essential for fast learning while the continuous output neuron was necessary for learning fine control. To achieve both fast learning and fine control, the shape of the sigmoid function in the continuous output neuron should be set before learning.     

Self-Tuning of a Neural Network Controller with an Integral Estimate of Contradictions between the Commands of the Learning Algorithm and Memory

We propose an approach to organizing self-tuning for a controller based on an artificial neural network that uses information on the contradictions arising in the creation of the value for the control signal between accumulated memory of the neural network and the learning algorithm based on backpropagation. The activity of neural network memory is estimated as its reaction to changing the state of the control system. Self-tuning is done by controlling the learning rate coefficient with an integral controller in order to stabilize the integral criterion for estimating the contradictions. Based on this modeling, we show a conceptual possibility for the operation of the self-tuning system with constant tuning parameters in a wide range of changes of the control object’s dynamical properties.     

基于Takagi-Sugeno的再励学习模糊神经网络控制

提出一种模糊神经网络的自适应控制方案。针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络,该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法。相应地,提出了一种优化学习算法,其把竞争式Takagi-Sugeno模糊再励学习网络训练成为一种所谓的Takagi-Sugeno模糊变结构控制器。以一级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法。     

结合Q学习和模糊逻辑的单路口交通信号自学习控制方法*

针对城市交通系统的动态性和不确定性,提出了基于强化学习的信号交叉口智能控制系统结构,对单交叉口动态实时控制进行了研究。将BP神经网络与Q学习算法相结合实现了路口的在线学习。同时,针对交通信号控制的多目标评价特征,采用基于模糊逻辑的Q学习奖惩信号设计方法,实施对交通信号的优化控制。最后,在三种交通场景下,应用Paramics微观交通仿真软件对典型十字路口进行仿真实验。结果表明,该方法对不同交通场景下的突变仍可保持较高的控制效率,控制效果明显优于定时控制。     

基于强化学习的倒立摆分数阶梯度下降RBF控制

为了提高强化学习的控制性能,提出一种基于分数梯度下降RBF神经网络的强化学习算法.通过评价神经网络和执行神经网络组成强化学习系统,利用神经网络记忆和联想,学会控制倒立摆,提高控制精度,使误差趋于零,直至学习成功,并证明闭环系统的稳定性.通过倒立摆的物理实验发现,当分数阶阶数较大,微分的作用更显著,对角速度和速度的控制效果更好,角速度和速度的均方误差和平均绝对误差较小;当分数阶阶数较小,积分的作用更显著,对倾斜角和位移的控制效果更好,因此倾斜角和位移的均方误差和平均绝对误差较小.仿真实验的结果表明,所提算法动态响应好,超调量小,调整时间短,精度高,泛化性能好.它优于基于RBF神经网络的强化学习算法和传统强化学习算法,能有效地加快梯度下降法的收敛速度,提高其控制性能.在引入适当的干扰后,所提算法能够快速地自我调节并恢复稳定状态,控制器的鲁棒性和动态性能满足实际要求.     

基于泛函分析的人工神经元模型改进研究

人工神经网络是对生物神经系统的模拟，它的信息处理功能与网络单元的特性密切相关。神经元的研究方兴未艾，神经元的效率问题有待提升。通过研究神经元学习算法，提高神经元的学习效率，并且提出一种新的神经元模型。     

A TSK-type recurrent fuzzy network for dynamic systems processingby neural network and genetic algorithms

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for the design of TRFN by either neural network or genetic algorithms depending on the learning environment. A recurrent fuzzy network is described which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and a neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, the TRFN-S displays both small network size and high learning accuracy. For problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, is characterized by high learning accuracy. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified