Small universal simple spiking neural P systems with weights

Spiking neural P systems with weights（WSN P systems,for short） are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given value.It is known that WSN P systems are universal by simulating register machines. However,in these universal systems,no bound is considered on the number of neurons and rules. In this work,a restricted variant of WSN P systems is considered,called simple WSN P systems,where each neuron has only one rule. The complexity parameter,the number of neurons,to construct a universal simple WSN P system is investigated. It is proved that there is a universal simple WSN P system with 48 neurons for computing functions; as generator of sets of numbers,there is an almost simple（that is,each neuron has only one rule except that one neuron has two rules） and universal WSN P system with 45 neurons.     

On languages generated by asynchronous spiking neural P systems

In this paper, we investigate the languages generated by asynchronous spiking neural P systems. Characterizations of finite languages and recursively enumerable languages are obtained by asynchronous spiking neural P systems with extended rules. The relationships of the languages generated by asynchronous spiking neural P systems with regular and non-semilinear languages are also investigated.     

Homogeneous spiking neural P systems working in sequential mode induced by maximum spike number

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire at each computation step, and neurons can be different in the sense that they can have different sets of spiking rules. In this work, we consider SN P systems with the restrictions: (1) all neurons are homogeneous in the sense that each neuron has the same set of rules; (2) at each step the neuron with the maximum number of spikes among the neurons that are active (can spike) will fire. These restrictions correspond to the fact that the system consists of only one kind of neurons and a global view of the whole network makes the system sequential. The computation power of homogeneous SN P systems working in the sequential mode induced by the maximum spike number is investigated. Specifically, it is proved that such systems are universal as both generating and accepting devices.     

Experiments on the reliability of stochastic spiking neural P systems

In the area of membrane computing, time-freeness has been defined as the ability for a timed membrane system to produce always the same result, independently of the execution times associated to the rules. In this paper, we use a similar idea in the framework of spiking neural P systems, a model inspired by the structure and the functioning of neural cells. In particular, we introduce stochastic spiking neural P systems where the time of firing for an enabled spiking rule is probabilistically chosen and we investigate when, and how, these probabilities can influence the ability of the systems to simulate, in a reliable way, universal machines, such as register machines.     

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

On spiking neural P systems and partially blind counter machines

A k-output spiking neural P system (SNP) with output neurons, , generates a tuple of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each O _i generates exactly two spikes aa (the times the pair aa are generated may be different for different output neurons) and the time interval between the first a and the second a is n _i . After the output neurons generate their pairs of spikes, the system eventually halts. We give characterizations of sets definable by partially blind multicounter machines in terms of k-output SNPs operating in a sequential mode. Slight variations of the models make them universal.     

Adaptive learning of fuzzy BSB and GBSB neural models

This article deals with a special class of neural autoassociative memory, namely, with fuzzy BSB and GBSB models and their learning algorithms. These models defined on a hypercube solve the problem of fuzzy clusterization of a data array owing to the fact that the vertices of the hypercube act as point attractors. A membership function is introduced that allows one to classify data that belong to overlapping clusters. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 18–28, November–December 2006.     

Uniform solutions to SAT and 3-SAT by spiking neural P systems with pre-computed resources

We consider the possibility of using spiking neural P systems for solving computationally hard problems, under the assumption that some (possibly exponentially large) pre-computed resources are given in advance. In particular, we propose two uniform families of spiking neural P systems which can be used to address the NP-complete problems sat and 3-sat, respectively. Each system in the first family is able to solve all the instances of sat which can be built using n Boolean variables and m clauses, in a time which is quadratic in n and linear in m. Similarly, each system of the second family is able to solve all the instances of 3-sat that contain n Boolean variables, in a time which is cubic in n. All the systems here considered are deterministic.     

Spiking neural P systems with neuron division and budding

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons.In this work,the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems,which are processes inspired by neural stem cell division. With neuron division and neuron budding,a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes.In this way,spiking neural P systems can efficie...     

一类死区非线性系统的自适应模糊控制设计

为了实现对具有时变摄动死区非线性系统的跟踪控制,本文提出了一种基于自适应模糊逼近器的Backstepping控制方法。该方法通过将死区特性合理分解,并将自适应模糊逼近器嵌入到Backstepping设计步骤中,逐步递推得到控制律。所提出的控制方法适用于高阶非线性系统,并且不要求被控系统满足匹配条件;所采用的模糊逼近器是非线性参数化的,亦即不要求其模糊基函数是完全确定已知的,从而降低了对先验知识的依赖性。为了得到未知参数的自适应律,本文先应用Taylor级数展开式将具有非线性关系的未知参数相互分离,使其呈现线性关系,然后根据Lyapunov稳定性定理给出在线可调参数的自适应律。此外,所设计的自适应律是对与未知参数向量的范数相关的变量进行在线调节,这样可以有效减少需要在线调节的参数数量,从而降低了控制器的在线计算负担,提高了系统的响应速度和控制精度。本文给出的控制设计能够有效地克服死区特性对系统性能的影响,使得闭环系统所有信号均指数收敛到原点的指定邻域内,系统输出可以按给定的精度跟踪参考信号。最后,本文用一个仿真实例验证了所给控制方法的有效性。     

A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule’s antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANFIS is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems.     

Characterizations of some classes of spiking neural P systems

We look at the recently introduced neural-like systems, called SN P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We study various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential SN P systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind counter machines and, hence, have many decidable properties. We also investigate the language-generating capability of SN P systems.     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

带反脉冲的同质脉冲神经膜系统

脉冲神经膜系统是一种膜系统中吸收了脉冲神经网络特点的新型生物计算装置,具有强大的计算能力.带反脉冲的同质脉冲神经膜系统是使用了两种对象(称为脉冲和反脉冲)、且其中每个神经元具有相同规则集合的一种脉冲神经膜系统的变体.本文研究了无延迟规则和突触权值情况下的带反脉冲同质脉冲神经膜系统的计算通用性问题,证明了这种P系统无论是工作在产生模式,还是接收模式下都是计算通用的.本文解答了曾湘祥等人提出的关于是否存在无延迟规则的同质脉冲神经膜系统和如何移除突触权值的两个公开问题.     

Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead-zone input

This paper is concerned with the problem of adaptive fuzzy output tracking control for a class of nonlinear pure-feedback stochastic systems with unknown dead-zone. Fuzzy logic systems in Mamdani type are used to approximate the unknown nonlinearities, then a novel adaptive fuzzy tracking controller is designed by using backstepping technique. The control scheme is systematically derived without requiring any information on the boundedness of dead-zone parameters (slopes and break-points) and the repeated differentiation of the virtual control signals. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighbourhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.     

自适应神经元模糊控制系统的研究

由于噪音、负载扰动等环境条件的变化,过程控制参数及模型结构往往会发生变化。为了提高控制器的性能,通过自适应神经元学习来修改模糊控制规则的控制方法。它通过总结过去控制规则的控制性能,对当前的控制规则进行调整,使之适应环境的变化,改善当前过程控制的输出。经仿真与实际检验,效果良好。     

An improvement on genetic-based learning method for fuzzy artificial neural networks

Fuzzy artificial neural networks (FANNs), which are the generalizations of artificial neural networks (ANNs), refer to connectionist systems in which all inputs, outputs, weights and biases may be fuzzy values. This paper proposes a two-phase learning method for FANNs, which reduces the generated error based on genetic algorithms (GAs). The optimization process is held on the alpha cuts of each fuzzy weight. Global optimized values of the alpha cuts at zero and one levels are obtained in the first phase and optimal values of several other alpha cuts are obtained in the second phase. Proposed method is shown to be superior in terms of generated error and executed time when compared with basic GA-based algorithms.     

On string languages generated by spiking neural P systems with exhaustive use of rules

We continue the study of (extended) spiking neural P systems with exhaustive use of rules by considering these computing devices as language generators. Specifically, a step is associated with a symbol according to the number of spikes emitted by the output neuron and the sequence of these symbols associated with a halting computation constitutes a string. Two cases are considered: one of them interprets a step when no spike is emitted as a specified symbol, the other interprets such a step as the empty string. In both cases, it is proved that finite and recursively enumerable languages are characterized by extended spiking neural P systems working in the exhaustive mode. The relationships with regular languages are also investigated.

脉冲神经膜系统求解任意两个自然数的乘积

考虑在一种新的生物计算装置(即脉冲神经膜系统)上处理任意两个自然数的乘积问题.首先给出了具有单个输入神经元的脉冲神经膜系统,它可以求解n-addition问题(即n个自然数的求和);其次,构造了一族脉冲神经膜系统,使该族中的每个系统可以求解给定二进制位长度的任意两个自然数的乘积.文中解决了Miguel AGutierrez-Naranjo和Alberto Leporati提出的一个公开问题.