Representing diverse mathematical problems using neural networks in hybrid intelligent systems

In recent years, artificial neural networks have attracted considerable attention as candidates for novel computational systems. Computer scientists and engineers are developing neural networks as representational and computational models for problem solving: neural networks are expected to produce new solutions or alternatives to existing models. This paper demonstrates the flexibility of neural networks for modeling and solving diverse mathematical problems including Taylor series expansion, Weierstrass's first approximation theorem, linear programming with single and multiple objectives, and fuzzy mathematical programming. Neural network representations of such mathematical problems may make it possible to overcome existing limitations, to find new solutions or alternatives to existing models, and to achieve synergistic effects through hybridization.     

基于测度递进的模糊认知图及其应用

模糊认知图(fuzzy cognitive map,FCM)具有简单的推理机制和较强的因果关系表达能力,已得到广泛关注和研究,但FCM对专家经验知识具有较强的依赖性,故而限制了在复杂动态系统建模中的应用.基于此,提出了一种测度递进策略的模糊认知图学习方法.利用线性回归算法,学习得到模糊认知图权重矩阵粗模型;将神经网络的权值调整算法应用于权重矩阵粗模型的细化过程,将该模糊认知图模型应用在股票市场,实现对股票日均值的预测.实验结果表明了该建模方式是有效的.     

基于免疫计算的Multi-agent系统设计方法

依据人工免疫系统的特点，分析和设计了免疫Agent的结构，建立了一种基于生物免疫机制的Multi-Agent系统网络模型，并给出了其形式化描述。以此为基础，阐述了构建具有更强的灵活性、鲁棒性和局部更新能力的复杂分布式软件系统的方法和步骤，这对现有的软件工程起到了重要的补充作用。     

模糊认知图在股票市场预测中的应用研究

复杂系统中存在着大量的过程依赖、自组织,并且一直是进化的,用传统的方法对其建模十分困难。模糊认知图作为一种模糊逻辑和神经网络相结合的产物,为复杂系统建模提供了一种有效工具。文中根据模糊认知图的特点,提出了用遗传学习算法建立系统的模糊认知图方法,为复杂系统分析及预测提供了一种解决方案。最后,以股票市场的数据为例进行了分析和预测模拟,结果表明,该方法是有效的。     

基于Petri Net的多Agent系统建模

多Agent系统是近年来分布式人工智能的一个研究热点。随着对其研究的深入和应用的推广,急需一种建模技术对其进行形式化描述。针对这个问题,该文提出了一种使用PetriNet形式化表示多Agent系统的方法,分别从单个Agent视图和整个Agent系统的全局视图对其加以描述,以便进一步的分析和评估。     

An experimental study of the dynamics of the certainty neuron fuzzy cognitive maps

Certainty Neurons have been introduced as a new type of artificial neurons that use a two variable transfer function that provides them with memory capabilities and decay mechanism. They are used in fuzzy cognitive maps which is an artificial neural network structure that creates models as collections of concepts – neurons and the various causal relationships – weighted arcs that exist between them. An experimental study of the certainty neuron fuzzy cognitive maps (CNFCMs) dynamical behaviour is presented as this appears through simulations. Two control parameters are used: the symmetry of the system's weight matrix and the strength of the decay mechanism. The values of these two parameters can lead the system to exhibit stable fixed point behaviour, limit cycle behaviour or to collapse. The ways that the two control parameters cause the change of the system's dynamical behaviour from fixed point to limit cycle are also presented. The areas where the systems exhibit specific dynamical behaviour are identified.     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.     

磁浮列车悬浮系统的神经网络建模研究

磁浮列车的悬浮系统是一个典型的非线性系统,其精确数学模型的建立非常困难。目前使用的系统模型大多是经过简化的近似线性化动力学模型,这样的模型在悬浮系统的研究中只起到方向上的指导作用,在工程实践中获取控制对象的精确模型具有重要的意义。神经网络不仅能够逼近复杂的非线性静态映射关系,同时也可以用于动态系统的特性学习,这里采用神经网络来建立悬浮系统的精确模型。文中简述了磁浮列车悬浮系统的基本结构和原理。讨论了非线性动态系统神经网络建模的一般方法。采用了输出反馈型的多层前向神经网络对悬浮系统进行了建模。并使用悬浮系统的输入输出数据对神经网络模型进行了训练和仿真,验证了该建模方法的可行性。     

Maintaining distributed logic programs incrementally

Distributed logic programming languages, which allow both facts and programs to be distributed among different nodes in a network, have been recently proposed and used to declaratively program a wide-range of distributed systems, such as network protocols and multi-agent systems. However, the distributed nature of the underlying systems poses serious challenges to developing efficient and correct algorithms for evaluating these programs. This paper proposes an efficient asynchronous algorithm to compute incrementally the changes to the states in response to insertions and deletions of base facts. Our algorithm is formally proven to be correct in the presence of message reordering in the system. To our knowledge, this is the first formal proof of correctness for such an algorithm.     

Indistinguishable from Magic: Computation is Cognitive Technology

This paper explains how mathematical computation can be constructed from weaker recursive patterns typical of natural languages. A thought experiment is used to describe the formalization of computational rules, or arithmetical axioms, using only orally-based natural language capabilities, and motivated by two accomplishments of ancient Indian mathematics and linguistics. One accomplishment is the expression of positional value using versified Sanskrit number words in addition to orthodox inscribed numerals. The second is Pāṇini’s invention, around the fifth century BCE, of a formal grammar for spoken Sanskrit, expressed in oral verse extending ordinary Sanskrit, and using recursive methods rediscovered in the twentieth century. The Sanskrit positional number compounds and Pāṇini’s formal system are construed as linguistic grammaticalizations relying on tacit cognitive models of symbolic form. The thought experiment shows that universal computation can be constructed from natural language structure and skills, and shows why intentional capabilities needed for language use play a role in computation across all media. The evolution of writing and positional number systems in Mesopotamia is used to transfer the thought experiment of “oral arithmetic” to inscribed computation. The thought experiment and historical evidence combine to show how and why mathematical computation is a cognitive technology extending generic symbolic skills associated with language structure, usage, and change.     

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.     

Specifying distributed multi-agent systems in chemical reaction metaphor

This paper presents an application of Chemical Reaction Metaphor (CRM) in distributed multi-agent systems (MAS). The suitability of using CRM to model multi-agent systems is justified by CRM's capacity in specifying dynamic features of multi-agent systems. A case study in an agent-based e-learning system (course material updating) demonstrates how the CRM based language, Gamma, can be used to specify the architectures of multi-agent systems. The effectiveness of specifying multi-agent systems in CRM from the view point of software engineering is further justified by introducing a transformational method for implementing the specified multi-agent systems. A computation model with a tree-structured architecture is proposed to base the design of the specified multi-agent system during the implementation phase. A module language based on the computation model is introduced as an intermediate language to facilitate the translation of the specification of multi-agent systems. The multicast networking technology pragmatizes the implementation of communications and synchronization among distributed agents. The paper also discusses the feasibility of implementing an automatic translation from the Gamma specification to a program in the module language. This work is supported by University of Houston-Downtown Organized Research Committee.     

人工生命与人工生命计算

论文从人工生命的角度定义了人工生命计算的概念,提出了人工生命计算的一般框架。人工生命计算是一种以人工生命为形式、研究人工生命的信息表达和处理机制,提取相应的计算模型,嵌入相应的计算方法模拟自然界生物进化过程与信息处理机制来求解与优化问题的智能计算方法。同时对人工生命计算的理论基础包括遗传算法、人工神经网络、自动机理论、L-系统、智能体和多智能体系统和计算生态学等进行了概述;并对两种典型的人工生命计算方法进行了初步的研究。最后说明了人工生命计算的特点及目前的应用领域。人工生命计算具有非常显著的特点和优点,在科学和工程的诸多实际应用领域具有广泛的应用前景。     

Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

直流无刷电机智能控制系统研究

阐述直流无刷电机工作原理,分析直流无刷电机的数学模型;介绍模糊控制理论与神经网络控制理论,提出模糊自适应PID控制策略;在MATLAB环境下,分别使用反电动势建模法建立直流无刷电机控制系统的模型,并对各个模型进行仿真分析。然后利用BP神经网络控制策略,模糊自适应PID控制策略改进速度控制器中的常规PID算法,进行仿真,并将所得结果进行对比。从对比结果可以得出模糊自适应PID控制策略更适合直流无刷电机的控制。     

催化裂化过程稳态优化控制研究

催化裂化装置是一个高度非线性、时变、长时延、强耦合、分布参数和不确定性的复杂 系统.在研究其过程机理的基础上,定义了一种模糊神经网络用以建模,用自相关函数检验法检 验模型的正确性,再用改进的Frank-Wolfe算法进行稳态优化计算,并以一炼油厂催化裂化装 置为对象进行试验,研究其辨识、建模和稳态优化控制.这种模糊神经网络具有隐层数多、隐层 结点数多、泛化能力和逼近能力强、收敛速度快的优点,更突出的特点还在于可由输出端对输入 求导,为稳态优化计算提供了极大方便,它与改进的Frank-Wolfe算法相结合用于解决非线性 复杂生产过程的建模和稳态优化控制问题是可行的.