关于神经系统自适应理论的研究

神经科学、心理学、人工智能和认知科学等学科的主要目标即是解释和模拟自然智能和人工智能。人脑是经过几十万年进化，或对自然界自适应过程的产物。因此，研究人脑的进化或自适应机理有极为重要的意义。本文首先讨论了目前所研究的一些核心问题及研究方法。然后从交叉学科的角度讨论了人脑神经系统的自适应理论。有关结果用规则形式给出以便于计算机或工程模拟。     

Perfect adaptation of general nonlinear systems

Perfect adaptation describes the ability of a biological system to restore its biological function precisely to the pre-perturbation level after being affected by the environmental disturbances. Mathematically, a biological system with perfect adaptation can be modelled as an input-output nonlinear system whose output, usually determining the biological function, is asymptotically stable under all input disturbances concerned. In this paper, a quite general input-output mathematical model is employed and the ‘functional’ of biological function (FBF) - output Lyapunov function - is explored to investigate its perfect adaptation ability. Sufficient condition is established for the systems with FBF to achieve perfect adaptation. Then a sufficient and necessary condition is obtained for the linear systems to possess an output Lyapunov function. Furthermore, it is shown that the ‘functional’ of receptors activity exists in the perfect adaptation model of E. coli chemotaxis. Different with the existing mathematical surveys on perfect adaptation, most of which are based on the standpoint of control theory, we first investigate this problem using ways of nonlinear systems analysis.     

ADAPTIVE SYSTEMS THEORY： SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system‘s structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some “critical values“ and establishing some “Impossibility Theorems“ for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.     

A Robust Adaptive Dynamic Programming Principle for Sensorimotor Control with Signal-Dependent Noise

As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.     

农业知识模型研究初探

将数学模型理论运用到知识表达中去,提出了知识模型表达方式思想,并阐述了农业知识模型的基本理论和实现方式,建立了作物气候适应性知识模型系统。参7。     

New type of adaptive control for a class of distributed time-delay systems with adaptation regard to delay parameter

The problem on stabilization for the system with distributed delays is researched. The distributed time-delay under consideration is assumed to be a constant time-delay, but not known exactly. A design method is proposed for a memory proportional and integral (PI) feedback controller with adaptation to distributed time-delay. The feedback controller with memory simultaneously contains the current state and the past distributed information of the addressed systems. The design for adaptation law to distributed delay is very concise. The controller can be derived by solving a set of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness of the design method.     

供应链的系统运作模式分析与建模——基于复杂自适应系统范式的研究

整体运作环境的变化使得传统的方法已经无法合理解决当今供应链管理中出现的复杂问题。应用复杂性科学的理论,本文以复杂自适应系统（Complexity Adaptive System, CAS）范式作为分析方法,系统性诠释了供应链的整体运作模式。文中首先以“适应性造就复杂性”的思想为指导,认为供应链是一个（CAS）,并建立了相应的概念模型,然后基于此模型,探讨了供应链管理的策略性原则。文章最后在桑塔费研究所开发的智能仿真平台-SWARM的基础上,建立了一个基于Multi-Agent的供应链仿真模型。研究表明该范式能够更好的分析与描述供应链这一复杂系统发生、创新、学习和适应等行为的本质。     

基于非线性输入控制实现受扰混沌系统同步

针对一类受扰混沌系统的同步控制,采用具有扇区非线性输入的自适应滑膜变结构控制器,设计了相应的控制律和自适应律。在该控制器的作用下,实现了驱动系统和响应系统的同步控制,且不受扇区非线性输入、参数失配及外部干扰的影响,具有有很强的鲁棒性。所设计的控制策略简单,易于实现,而且没有强加在系统上的限制条件,因此应用范围较广。理论分析和数值仿真证明了该方法的有效性。     

基于rank自适应的3D MIMO有限反馈传输方案

大规模多输入多输出 (multiple-input multiple-output,MIMO) 技术能够提高频谱效率和能量效率,被认为是下一代无线通信的关键技术。随着有源天线技术的发展,基站配置二维天线阵列可在三维(three-dimensional,3D)空间控制波束,这使得3D MIMO技术成为一个新的研究热点。在单用户MIMO下,本文提出了两种基于秩(rank)自适应的3D MIMO有限反馈方案。方案I在水平维采用rank自适应,垂直维支持rank 1传输;方案II在垂直维采用rank自适应,水平维支持rank 1传输。在三维城市宏小区和三维城市微小区场景下,采用系统级仿真验证了两种方案的性能。相较于水平维和垂直维都仅支持rank 1的传输方案,两种方案都取得了性能增益,同时方案I优于方案II。     

基于遗传算法的生物网络自适应负载平衡实现

自适应性网络环境将成为未来Internet的不可缺少的重要构成部分,而生物网络由分散的、自治、移动的个体组成,能够自我调整、适应和生存。在提出的生物网络框架中设计了一种特殊的生物实体——调度生物实体,利用调度实体来指导生物实体的移动,以期获得生物网络的负载平衡。然后提出了一种基于遗传算法的负载平衡算法,该算法以网络负载平衡为优化目标,使实体相对均衡地提供服务,达到合理利用生物网络资源,增强其自适应性的目的。最后,对网络服务使用进行仿真,实验结果证明了算法的有效性。     

基于正交函数网络的不确定混沌系统的控制

提出了基于正交函数网络的不确定混沌系统的自适应控制方法．通过利用计算简单、收敛速度快的单隐层正交函数神经网络，构建了一类不确定混沌系统的控制器．利用李雅普诺夫稳定性定理得到了该网络控制器的权值更新规则并保证了权值误差和跟踪误差的有界性．该控制器不仅能够保证混沌系统以有界误差对指定轨迹进行精确跟踪，也能够使有外部扰动的混沌系统快速跟踪一个指定的轨迹．最后，利用陈氏混沌系统和Lorenz系统进行了系统仿真，结果表明了该控制器在混沌控制中的有效性。     

HEALTHCARE： A COMPLEX SERVICE SYSTEM

Healthcare is indeed a complex service system, one requiring the technobiology approach of systems engineering to underpin its development as an integrated and adaptive system. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components — people (characterized by behaviors, values, knowledge, etc.), processes (characterized by collaboration, customization, etc.) and products (characterized by software, hardware, infrastructures, etc.). Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency (leading to greater interdependency) and effectiveness (leading to improved health). Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.     

人体手部运动的振荡共振辅助系统实验研究

劳动分工、生活节奏的加快以及疾病等因素使得人体手部神经系统对外界感知灵敏度降低,也导致了中国人体运动障碍的患者数目庞大。针对这类因素导致的手部运动稳定性降低的问题,设计了一套振荡共振辅助系统,对于人体手部运动进行了生理实验研究。该辅助运动系统由多个微电机形成高频振荡源,将不同振动强度的振荡信号作用于手背部。对手部运动的方差和相关时间分析,证明振荡共振辅助系统能够提高手部神经系统的反应灵敏度,增强手部运动的平稳性,为人体医学康复仪器设计和制作提供了很好的生理实验基础。     

含体液因子影响的神经网络仿真

调节人的机体活动的方式除了神经调节还有体液调节等方式。本文将体液调节与人工神经网络相结合,对一种结合体液调节的人工神经网络机制进行了仿真(相当于内环境可变的神经网络系统)。在不考虑神经系统对体液系统反作用时,对BP神经网络的受体液环境参数影响的阈值参数学习公式进行了推导。接着,考虑到神经网络对体液系统的反作用,以闭环方式实现了一个含体液因子的神经网络系统;此闭环方式可以对神经网络模型的误差进行自动补偿,属于自适应神经网络的范畴。并以麻醉剂模式对这种自适应神经网络的成立条件进行推导,对其稳定性进行了验证。最后对这些含体液因子的神经网络的阈值参数进行灰延拓,拓展其成其为含灰色体液因子的神经网络系统;并把这种含灰色体液因子的神经网络系统在大规模定制生产过程的质量控制中进行了应用。     

论企业文化的整体性与个体的自适应性

运用复杂适应系统理论,论述了企业文化的整体性与个体文化的差异性,并对有差异的个体文化通过自适应促进企业整体性文化的形成进行了描述。     

语音转换技术在电话语音识别中的应用研究

提出了一种用语音转换技术改善电话语音识别性能的方法。通过模拟真实电话信道条件下影响语音质量的各种因素，实现由纯净语音到电话语音的转换。识别试验利用模拟电话语音评估了HMM识别器做MLLR自适应前后的性能．实验数据显示，自适应前由转换语音训练的模型识别率比由纯净语音训练的模型识别率增加了18．9％，而自适应试验表明，由转换语音训练而成的模型在MLLR自适应后，系统识别性能进一步得到改善，识别率增加了5．8％。识别实验表明所提语音转换方法可以减小由于真实电话语料缺乏而造成训练语音和测试语音声学性质的不匹配，从而有效地改善电话语音识别系统的性能。     

改进的舰船维修费案例调整模型

为提高舰船维修费案例的调整效果,遵循着调整知识产生、调整知识约简和调整知识推理的建模过程,提出了一种改进的舰船维修费案例调整模型。在该模型中,采用Policastro的重构方式,利用目标案例的直接和间接相似案例集,产生舰船维修费案例的调整知识;针对调整知识中可能存在冗余、相关性、知识量过于庞大不便于推理等问题,提出了一种既能有效约简调整知识、又能准确衡量调整知识重要度的改进规则约简方法,并给出了具体的实现算法;为提高知识推理的效率,采用具有强泛化能力和出色学习能力的最小二乘支持向量机（least square support vector machine,LSSVM）,构建了舰船维修费案例调整知识推理模型,并基于遗传算法（genetic algorithm,GA）和交叉验证方法,对推理模型的有关参数进行了优化。实例结果表明,与其他案例调整模型相比,所提模型在调整精度上要远远好于其他模型,所提模型是科学、有效的。     

On integration and adaptation in complex service systems

The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization （whereby a service is produced and delivered in response to a customer＇s stated or imputed needs）. In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines （e.g., physics, mathematics, statistics, psychology, sociology, etc.）. In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxon     

无人飞行器分布式控制系统集成新技术

无人飞行器（UAV－Unmanned Aerial Vehicles）自主飞行控制系统是一复杂控制系统，需要集成新的控制算法与各种不同组件技术和资源。来自不同类型硬件平台的组件，可能由不同的操作系统支持。分布在不同环境的组件能灵活配置和集成，使系统对环境变化和／或没预测到的事件能够快速响应，实现UAV在线重配和自适应能力。软件技术的最新进展可使飞行控制系统设计发生革命性变化。本文着重介绍一种用于UAV飞控系统集成的全新的开放控制平台（OCP－Open Control Platform），在介绍第一个OCP应用原型后，给出了下一步要做的工作。     

Enabling Political Legitimacy and Conceptual Integration for Climate Change Adaptation Research within an Agricultural Bureaucracy: a Systemic Inquiry

The value of using systems approaches, for situations framed as ‘super wicked’, is examined from the perspective of research managers and stakeholders in a state-based climate change adaptation (CCA) program (CliChAP). Polycentric drivers influencing the development of CCA research pre-2010 in Victoria, Australia are reflected on, using Soft Systems Methodology (SSM) to generate a boundary critique of CCA research as a human activity system. We experienced the complexity of purpose with research practices pulling in different directions, reflected on the appropriateness of agricultural bureaucracies’ historical new public management (NPM) practices, and focused on realigning management theory with emerging demands for adaptation research skills and capability. Our analysis conceptualised CliChAP as a subsystem, generating novelty in a wider system, concerned with socio-ecological co-evolution. Constraining/enabling conditions at the time dealing with political legitimacy and conceptual integration were observed as potential catalysts for innovation in research management towards better handling of uncertainty as a social process using systemic thinking in practice (StiP).