Chaos control and associative memory of a time-delay globally coupled neural network using symmetric map

A chaotic neural network called time-delay globally coupled neural network using symmetric map (TDSG) is proposed for information processing applications. Firstly, its rich dynamic behaviors are exhibited and the output stability is demonstrated by using a parameter modulated control method. Secondly, the associative memory of TDSG is investigated by the control method. It is observed that the stable output sequence only contains stored pattern and its reverse pattern and the TDSG finally converges to the stored pattern which has the smallest Hamming distance to the initial patterns with noise. At last, strong information recovery ability of the TDSG is illustrated by comparative experiments.     

Operation of an associative memory

This paper describes the operation of an associative memory (LYAM) governed by only ordinary differential equations, useful for pattern clustering. Several computer simulations illustrate its operation as an unsupervised classifier, vector quantizer, and content-addressable memory.     

A property of neural networks of associative memory with replacing units

A memory capacity exists for artificial neural networks of associative memory. The addition of new memories beyond the capacity overloads the network system and makes all learned memories irretrievable (catastrophic forgetting) unless there is a provision for forgetting old memories. This article describes a property of associative memory networks in which a number of units are replaced when networks learn. In our network, every time the network learns a new item or pattern, a number of units are erased and the same number of units are added. It is shown that the memory capacity of the network depends on the number of replaced units, and that there exists a optimal number of replaced units in which the memory capacity is maximized. The optimal number of replaced units is small, and seems to be independent of the network size. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

An adaptive associative memory system based on autonomous reaction between image memories

To construct a “thinking-like” processing system, a new architecture of an adaptive associative memory system is proposed. This memory system treats “images” as basic units of information, and adapts to the environment of the external world by means of autonomous reactions between the images. The images do not have to be clear, distinct symbols or patterns; they can be ambiguous, indistinct symbols or patterns as well. This memory system is a kind of neural network made up of nodes and links called a localist spreading activation network. Each node holds one image in a localist manner. Images in high-activity nodes interact autonomously and generate new images and links. By this reaction between images, various forms of images are generated automatically under constraints of links with adjacent nodes. In this system, three simple image reaction operations are defined. Each operation generates a new image by combining pseudofigures or features and links of two images. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

连续学习混沌神经网络的研究

近几年混沌神经网络在信息处理,特别是联想记忆中的应用得到了极大重视。本文提出了一个改进的连续学习混沌神经网络(MSLCNN)模型,它具有两个重要特征：(1)根据不同的输入,神经网络做出不同的响应,可从已知模式来识别未知模式;(2)可连续学习未知模式。计算机仿真表明我们的模型具有应用潜力。     

动态核的形态联想记忆网络在灰度图中的应用

介绍了应用于灰度图像的联想记忆和识别的动态核方法,给出了动态核选择的原则和途径。利用动态核可以解决灰度图像在含有随机噪声时的自联想记忆和识别问题,从而给出了一种较好地处理含噪灰度图像恢复的途径。通过实验,验证了该方法的良好性能,取得了较理想的结果。     

Time of searching for similar binary vectors in associative memory

Times of searching for similar binary vectors in neural-net and traditional associative memories are investigated and compared. The neural-net approach is demonstrated to surpass the traditional ones even if it is implemented on a serial computer when the entropy of a space of signals is of order of several hundreds and the number of stored vectors is vastly larger than the entropy. This work is supported by RFBR grant 05-07-90049 and partially by the Center of Applied Cybernetics under grant No. 1M6840070004 (Institutional Research Plan AV0Z10300504). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 3–13, September–October 2006.     

基于模式关联的一对多联想记忆方法

在现有的多模块一对多联想记忆模型中,由于所处理的记忆模式集合本身的特点以及记忆模式之间的关联被忽视,使得构造出来的模型结构复杂,难以实际应用.针对这一不足,提出一种基于模式关联的实现方法.以该方法构造出的多模块一对多联想记忆模型结构简单,易于硬件实现,使得多模块一对多联想记忆模型具有了实际应用的可能.     

动态核的形态分解联想算法及抗随机噪声研究

在灰度图像分解算法和动态核形态联想记忆网络的基础上,提出了一种新的联想记忆算法--动态核的形态分解联想算法.该方法显著地提高了联想记忆抗随机噪声的能力,较好地解决了灰度图像在含噪时的联想记忆和识别的问题,从而给出了一种恢复含噪灰度图像的途径,并把该方法推广到了彩色图像的处理.通过实验,验证了该方法的良好性能,取得了理想的结果.     

DMM:A dynamic memory mapping model for virtual machines

Memory virtualization is an important part in the design of virtual machine monitors(VMM).In this paper,we proposed dynamic memory mapping(DMM) model,a mechanism that allows the VMM to change the mapping between a virtual machine's physical memory and the underlying hardware resource while the virtual machine is running.By utilizing DMM,the VMM can implement many novel memory management policies,such as Demand Paging,Swapping,Ballooning,Memory Sharing and Copy-On-Write,while preserving compatibility with va...     

Sequence memory based on an oscillatory neural network

In the brain,the discrete elements in a temporal order is encoded as a sequence memory.At the neural level,the reproducible sequence order of neural activity is very crucial for many cases.In this paper,a mechanism for oscillation in the network has been proposed to realize the sequence memory.The mechanism for oscillation in the network that cooperates with hetero-association can help the network oscillate between the stored patterns,leading to the sequence memory.Due to the oscillatory mechanism,the firing history will not be sampled,the stability of the sequence is increased,and the evolvement of neurons’states only depends on the current states.The simulation results show that neural network can effectively achieve sequence memory with our proposed model.     

A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory

The purpose of this study is to investigate a new representation of shape and its use in handwritten online character recognition by a Kohonen associative memory. This representation is based on the empirical distribution of features such as tangents and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a Kohonen neural network trained using the representation. In addition to the Euclidean distance traditionally used in the Kohonen training algorithm to measure the similarities among feature vectors, we also investigate the Kullback–Leibler divergence and the Hellinger distance, functions that measure distance between distributions. Furthermore, we perform operations (pruning and filtering) on the trained memory to improve its classification potency. We report on extensive experiments using a database of online Arabic characters produced without constraints by a large number of writers. Comparative results show the pertinence of the representation and the superior performance of the scheme.     

A short term memory for Neural Networds which allows recognition and reproduction of complex sequences of integers with the minimum number of weights

An artificial short term memory, the binary kernel function, is presented to facilitate the learning of complex sequences of integers by Neural Networks, requiring far fewer weights than are usually needed. This is achieved by using only a single weight to encode repeat occurrences of an integer in a sequence. The coding used allows a complex sequence to be learned in only one presentation. The kernel's exponential complexity growth is overcome with hierarchical architectures which chunk the sequences to be learnt. Architectures are introduced for recognition and reproduction of complex sequences.     

基于RCGA优化GRNN的实时动态目标追踪模型

针对三维环境中导弹追踪目标时制导和控制算法复杂而导致计算量非常大的问题,提出了一种基于隐性交叉遗传算法优化广义回归神经网络的实时动态目标追踪模型。通过将导弹防御区离散化为多个小模块生成输入数据,并针对每个可接受的目标参数数据集,使用RCGA估算导航常量和导弹注意时间;利用输入和输出的目标参数集生成GRNN所需的训练数据集;针对任意位置的目标轨道,将训练后的GRNN应用于实时导弹导引系统的实现中。通过战术目标仿真模型验证了所提算法的有效性及可靠性,仿真结果表明,相比其他几种目标追踪算法,算法取得了更好的实时性和更高的目标定位精度,脱靶率接近零。     

Integrated neural and robotic simulations. Simulation of cerebellar neurobiological substrate for an object-oriented dynamic model abstraction process

Experimental studies of the Central Nervous System (CNS) at multiple organization levels aim at understanding how information is represented and processed by the brain’s neurobiological substrate. The information processed within different neural subsystems is neurocomputed using distributed and dynamic patterns of neural activity. These emerging patterns can be hardly understood by merely taking into account individual cell activities. Studying how these patterns are elicited in the CNS under specific behavioral tasks has become a groundbreaking research topic in system neuroscience. This methodology of synthetic behavioral experimentation is also motivated by the concept of embodied neuroscience, according to which the primary goal of the CNS is to solve/facilitate the body–environment interaction.With the aim to bridge the gap between system neuroscience and biological control, this paper presents how the CNS neural structures can be connected/integrated within a body agent; in particular, an efficient neural simulator based on EDLUT (Ros et al., 2006) has been integrated within a simulated robotic environment to facilitate the implementation of object manipulating closed loop experiments (action–perception loop). This kind of experiment allows the study of the neural abstraction process of dynamic models that occurs within our neural structures when manipulating objects.The neural simulator, communication interfaces, and a robot platform have been efficiently integrated enabling real time simulations. The cerebellum is thought to play a crucial role in human-body interaction with a primary function related to motor control which makes it the perfect candidate to start building an embodied nervous system as illustrated in the simulations performed in this work.     

A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment

A hybrid flow shop (HFS) is a generalized flow shop with multiple machines in some stages. HFS is fairly common in flexible manufacturing and in process industry. Because manufacturing systems often operate in a stochastic and dynamic environment, dynamic hybrid flow shop scheduling is frequently encountered in practice. This paper proposes a neural network model and algorithm to solve the dynamic hybrid flow shop scheduling problem. In order to obtain training examples for the neural network, we first study, through simulation, the performance of some dispatching rules that have demonstrated effectiveness in the previous related research. The results are then transformed into training examples. The training process is optimized by the delta-bar-delta (DBD) method that can speed up training convergence. The most commonly used dispatching rules are used as benchmarks. Simulation results show that the performance of the neural network approach is much better than that of the traditional dispatching rules.This revised version was published in June 2005 with corrected page numbers.     

A profiling method by PCB hooking and its application for memory fault detection in embedded system operational test

Context

An operational test means a system test that examines whether or not all software or hardware components comply with the requirements given to a system which is deployed in an operational environment.

Objective

It is a necessary lightweight-profiling method for embedded systems with severe resource restrictions to conduct operational testing.

Method

We focus on the Process Control Block as the optimal location to monitor the execution of all processes. We propose a profiling method to collect the runtime execution information of the processes without interrupting the system’s operational environment by hacking the Process Control Block information. Based on the proposed method applied to detect runtime memory faults, we develop the operational testing tool AMOS v1.0 which is currently being used in the automobile industry.

Results

An industrial field study on 23 models of car-infotainment systems revealed a total of 519 memory faults while only slowing down the system by 0.084-0.132×. We conducted a comparative analysis on representative runtime memory fault detection tools. This analysis result shows our proposed method that has relatively low overhead meets the requirements for operational testing, while other methods failed to satisfy the operational test conditions.

Conclusion

We conclude that a lightweight-profiling method for embedded system operational testing can be built around the Process Control Block.     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

A novel model by evolving partially connected neural network for stock price trend forecasting

This paper proposes a novel model by evolving partially connected neural networks (EPCNNs) to predict the stock price trend using technical indicators as inputs. The proposed architecture has provided some new features different from the features of artificial neural networks: (1) connection between neurons is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and training weights. In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is defined using sin(x) function instead of sigmoid function. Three experiments were conducted which are explained as follows. In the first experiment, we compared the predicted value of the trained EPCNN model with the actual value to evaluate the prediction accuracy of the model. Second experiment studied the over fitting problem which occurred in neural network training by taking different number of neurons and layers. The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data. Therefore, it is a very promising tool in forecasting of the financial time series data.     

A neural network model of the olfactory system of mice: computer simulation of the attention behavior of mice for some components in an odor

Recently, it was observed that mice could identify an odor by paying attention to only a few of its components. Further, it has been reported that each individual is attracted to different components of an odor. This behavior is referred to as “attention”; however, its mechanism has yet to be completely elucidated. In this paper, we first propose a novel artificial neural network model based on the biological structure of an olfactory system. Then a series of computer simulations of odorant discrimination are performed to evaluate the attention ability of the proposed model. Finally, we changed the connective weights between the neurons to simulate individual differences. The simulation results indicate that the inhibitory connections from the piriform cortex to the olfactory bulb may contribute to the individual differences observed in the behavioral experiment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007