用二值逻辑对多值逻辑进行优化

提出以二值多输出逻辑优化软件OPLG为基础，对多值逻辑函数进行逻辑优化的方法．通过对多值变量、多值函数的二进制矢量描述，将多值多维体转换为布尔表达式积项形式，从多值多维体的多值最小项出发，给出计算基本无关集的方法。对多值逻辑函数的优化通过调用二值逻辑优化软件OPLG(允许的最大输入、输出变量之和为300)来实现，二值逻辑优化的结果最终再转换为多值多维体的表示形式。     

基于多值方法的模糊逻辑函数最小化

首先提出了模糊逻辑和多值逻辑的相似性，并从开关信号理论出发建立了三值逻辑函数阈运算和模糊逻辑函数文字运算的对应关系，进而提出了基于差动电流开关理论的三值逻辑函数化简法求模糊逻辑函数最小化表达式的算法，并用该算法对几个模糊逻辑函数实例进行了化简，实例操作表明，该算法具有操作简单快捷的特点，是获得模糊逻辑函数最小化表达式的一种有效的方法。     

三值边沿触发器的研究

本文从实用考虑出发,利用时钟信号的竞争提出三值边沿触发器的逻辑设计,从而完整了对三轨三值触发器系列的研究。 作为多值数字系统中的关键部件,对多值存储单元的研究一直受到重视。研究表明,如果它们能用传统的集成技术与二值存储单元制作在同一芯片上,则可构成冗余态较少,可靠性较强,及电路较简单的混值计数器,这种计数器可以在系统中替代相应的二     

函数链神经网络的性能改进

函数链网络（Ｆｕｎｃｔｉｏｎａｌ Ｌｉｎｋ Ｎｅｔｗｏｒｋ－ＦＬＮ）通过对向量（或模式）的非线性扩展,将非线性照射特性引入了单层神经网络,采用δ学习规则获得了快速的学习和非线性映射特性。文中在ＦＬＮ基础上,借助凸集优化思想,利用最陡梯度下降技术获得了比ＦＬＮ更高的存储容量和更快速的学习速度。计算机模拟的结果证实了所提的算法性能。     

关于结构奇异值的一个注记

１引言Ｄｏｙｌｅ在１９８２年提出的结构奇异值（μ）方法是分析和综合结构式不确定系统的有力工具［１，２］．基于结构奇异值分析的小μ定理［２］给出了具有多个摄动块的线性动态系统鲁棒稳定的充要条件．而鲁棒性能定理［２］则进一步地将鲁棒稳定性问题和鲁棒性能问题统一成μ分析问题．然而．我们注意到，在所有研究结构奇异值的文献中，均要求块对角摄动矩阵中每个子摄动块是方的．这一要求无疑大大限制了μ方法的应用，因为非方摄动块在系统中是经常存在的．此时对Ｄｏｙｌｅ给出的结构奇异值的上界函数［１］必须进行修正．２非方…     

一种分式过程神经元网络及其应用研究

针对带有奇异值复杂时变信号的模式分类和系统建模问题，提出了一种分式过程神经元网络．该模型是基于有理式函数具有的对复杂过程信号的逼近性质和过程神经元网络对时变信息的非线性变换机制构建的。其基本信息处理单元由两个过程神经元成对偶组成。逻辑上构成一个分式过程神经元，是人工神经网络在结构和信息处理机制上的一种扩展．分析了分式过程神经元网络的连续性和泛函数逼近能力，给出了基于函数正交基展开的学习算法．实验结果表明，分式过程神经元网络对于带有奇异值时变函数样本的学习性质和泛化性质要优于BP网络和一般过程神经元网络。网络隐层数和节点数可较大减少，且算法的学习性质与传统BP算法相同．     

对称三值逻辑及对称三值CMOS电路

本文从负数表示的研究引入对称三进制系统与对称三值逻辑.基于作者提出的传输函数理论,本文讨论了基本对称三值运算的CMOS电路实现,并已用计算机模拟证明它们具有正确的逻辑功能与理想的DC传输特性.基于这些基本电路单元,本文进一步设计了实现加法与乘法的两种对称三值运算单元.     

输入端加译码器的可编程逻辑阵列的复杂性分析

输入端加译码器的可编程逻辑阵列比普通的可编程逻辑阵列具有更大的实现能力。这种阵列表现为三级或－与－或电路。本文提出了与该电路相关的一系列基本概念和理论，并且还进行了复杂性分析，结论是使用该阵列实现一个任意Ｎ变量逻辑函数所需的最大存储单元数为：（２ｎ＋１）２＾ｎ－２．     

通用隐式动值查找窗口程序

该文在分析传统显式静值查找的基础上，介绍了ＩＮＫＥＯ（）函数实现记录隐式动值查找的方法，并给出ＦｏｘＰｒｏ通用窗口程序。     

P值逻辑函数的扩散性质

满足K次扩散准则的p值逻辑函数在密码设计中有重要应用。该文采用Znp上的置换定出了一类满足2n次扩散准则的p值逻辑函数,即Bent函数;定出了级联函数满足K次扩散准则的充要条件和n元2次p值逻辑函数满足m阶K次扩散准则的充要条件。     

具有学习功能的多值模糊神经网络控制系统

对于具有非线性、大时滞、不确定性等特性的难以用精确数学模型描述的多变量复杂系统,靠传统控制理论难以获得理想的控制效果。基于模糊神经网络控制技术不依赖于被控对象精确的数学模型,且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性,进行了采用模糊神经网络控制器实现其控制的应用研究。采用典型的前向型模糊神经网络模型,给出了具有学习功能的多值模糊神经网络控制系统的一种设计方法。仿真实验证明,该系统能够获得较理想的控制效果。     

Bi-Decomposition of Function Sets in Multiple-Valued Logic for Circuit Design and Data Mining

This article presents a theory for the bi-decomposition of functions in multi-valued logic (MVL). MVL functions are applied in logic design of multi-valued circuits and machine learning applications. Bi-decomposition is a method to decompose a function into two decomposition functions that are connected by a two-input operator called gate. Each of the decomposition functions depends on fewer variables than the original function. Recursive bi-decomposition represents a function as a structure of interconnected gates. For logic synthesis, the type of the gate can be chosen so that it has an efficient hardware representation. For machine learning, gates are selected to represent simple and understandable classification rules. Algorithms are presented for non-disjoint bi-decomposition, where the decomposition functions may share variables with each other. Bi-decomposition is discussed for the min- and max-operators. To describe the MVL bi-decomposition theory, the notion of incompletely specified functions is generalized to function intervals. The application of MVL differential calculus leads to particular efficient algorithms. To ensure complete recursive decomposition, separation is introduced as a new concept to simplify non-decomposable functions. Multi-decomposition is presented as an example of separation. The decomposition algorithms are implemented in a decomposition system called YADE. MVL test functions from logic synthesis and machine learning applications are decomposed. The results are compared to other decomposers. It is verified that YADE finds decompositions of superior quality by bi-decomposition of MVL function sets.     

A novel fuzzy logic system based on N-version programming

For the consideration of different application systems, modeling the fuzzy logic rule, and deciding the shape of membership functions are very critical issues due to they play key roles in the design of fuzzy logic control system. This paper proposes a novel design methodology of fuzzy logic control system using the neural network and fault-tolerant approaches. The connectionist architecture with the learning capability of neural network and N-version programming development of a fault-tolerant technique are implemented in the proposed fuzzy logic control system. In other words, this research involves the modeling of parameterized membership functions and the partition of fuzzy linguistic variables using neural networks trained by the unsupervised learning algorithms. Based on the self-organizing algorithm, the membership function and partition of fuzzy class are not only derived automatically, but also the preconditions of fuzzy IF-THEN rules are organized. We also provide two examples, pattern recognition and tendency prediction, to demonstrate that the proposed system has a higher computational performance and its parallel architecture supports noise-tolerant capability. This generalized scheme is very satisfactory for pattern recognition and tendency prediction problems     

基于模糊RBF神经网络的非线性滤波

该文从基本的智能技术——神经网络(NN)和模糊系统(FS)技术出发,探讨了神经网络与模糊系统相结合的基本理论,提出了一种基于模糊RBF神经网络的非线性滤波的方法。该方法将模糊逻辑的知识表达以及推理能力和RBF网络的快速学习和泛化能力结合起来,网络结构参数可按实际问题调整,对信号中有色噪声进行较高精度的逼近,来达到非线性滤波的目的。该滤波方法显示出很强的处理问题的能力,学习速度快,仿真结果表明了这种方法的有效性和可性行。     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

Projection-Based Adaptive Neurocontrol With Switching Logic Deadzone Tuning

In this brief, an adaptive neural network (NN) controller is proposed for multiple-input-multiple-output (MIMO) nonlinear systems with triangular control structure and unknown control directions. Deadzones are employed in the projection-based NN weight learning laws and the Nussbaum parameter update laws with levels tuned by an innovative switching logic tuning mechanism. Detailed analysis using a family of Lyapunov-like integral functions and the function approximation capability of NNs proves that all the tracking errors are semiglobal uniform ultimate bounded in small neighborhoods of the origin while the closed-loop system variables (state vector, NN weights, Nussbaum parameters) and the control law remain bounded. A simulation study confirms the theoretical results and verifies the effectiveness of the proposed design.     

Capacity analysis of the asymptotically stable multi-valuedexponential bidirectional associative memory

The exponential bidirectional associative memory (eBAM) has been proposed and proved to be a stable and high capacity associative neural network. However, the intrinsic structure and the evolution functions of this network restrict the representation of patterns to be either bipolar or binary vectors. We consider the promising development of multi-valued systems and then design a multi-valued discrete eBAM (MV-eBAM). The multi-valued eBAM has been proved to be asymptotically stable under certain constraints. Although MV-eBAM is also verified to possess high capacity by thorough simulations, there are important characteristics to be explored, including the absolute lower bound of the radix, and the approximate capacity. In order to estimate the capacity of the MV-eBAM, a modified evolution equation is also proposed. Hence, an analytic solution is derived. Besides, a radix searching algorithm is presented such that the absolute lower bound of the radix for this MV-eBAM can be found.     

Implementation of the RBF neural chip with the back-propagation algorithm for on-line learning

This article presents the hardware implementation of the floating-point processor (FPP) to develop the radial basis function (RBF) neural network for the general purpose of pattern recognition and nonlinear control. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel calculation of the back-propagation algorithm. Internal weights of the RBF network are updated by the online learning back-propagation algorithm. The on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning process written in the MATLAB program. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the XOR logic. Performances are evaluated by comparing results from the MATLAB through extensive experimental studies.     

Nonlinear discrete-time controller with unknown systems identification based on fuzzy rules emulated network

This article introduces an adaptive controller for a class of unknown nonlinear discrete-time systems based on multi-input fuzzy rules emulated network (MIFREN). By the estimation of any nonlinear systems from MIFREN, this network is assigned to identify the unknown system under control. The proposed control law is introduced by the result of nonlinear system identification based on MIFREM and the defined sliding condition. Without the need of any off-line learning phase, all control parameters including the learning rate for MIFREN are selected to guarantee the bonded signals such as the model error, tuned weight vector, the tracking error and the sliding signal via the defined Lyapunov functions and proposed theorems. The performance of the proposed control algorithm is demonstrated and the main theorem is validated by computer simulation results.     

Direct adaptive fuzzy backstepping control of uncertain nonlinear systems in the presence of input saturation

In this paper, a novel direct adaptive fuzzy control approach is presented for uncertain nonlinear systems in the presence of input saturation. Fuzzy logic systems are directly used to tackle unknown nonlinear functions, and the adaptive fuzzy tracking controller is constructed by using the backstepping recursive design techniques. To overcome the problem of input saturation, a new auxiliary design system and Nussbaum gain functions are incorporated into the control scheme, respectively. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of the origin. A simulation example is included to illustrate the effectiveness of the proposed approach. Two key advantages of the scheme are that (i) the direct adaptive fuzzy control method is proposed for uncertain nonlinear system with input saturation by using Nussbaum function technique and (ii) The number of the online adaptive learning parameters is reduced.