Intelligent data structures selection using neural networks

It is well known that abstract data types represent the core for any software application, and a proper use of them is an essential requirement for developing a robust and efficient system. Data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. Selecting and creating the appropriate data structure for implementing an abstract data type can greatly impact the performance and the efficiency of the software systems. It is not a trivial problem for a software developer, as it is hard to anticipate all the use scenarios of the deployed application, and a static selection before the system’s execution is, generally, not accurate. In this paper, we are focusing on the problem of dynamic selection of efficient data structures for abstract data types implementation using a supervised learning approach. In order to dynamically select the most suitable representation for an aggregate according to the software system’s current execution context, a neural network will be used. We experimentally evaluate the proposed technique on a case study, emphasizing the advantages of the proposed model in comparison with existing similar approaches.     

不确定混沌系统的动态神经网络跟踪控制

针对不确定非线性混沌系统，提出了一种基于动态神经网络辨识器的自适应跟踪控制新方法，通过滑模控制技术在线调整动态神经网络辨识器权值，并在获取动态神经网络模型的基础上设计出优化控制器，实现混沌系统的轨道跟踪，对辨识误差和轨道跟踪误差进行分析并证明了它们的有界性，Lorenz混沌系统的仿真实验结果表明了控制策略的有效性。     

Chaotic neural networks and chaotic annealing

A simple model of single neuron with chaotic dynamics is proposed. Neural networks coupled by such neurons have the property of temporal retrieval of stored patterns in a chaotic way. The network is also studied from the viewpoint of optimization. A chaotic annealing technique is developed to search for the global minima of the energy with transient chaos.     

Learning chaotic attractors by neural networks

An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored that tests the hypothesis that the reconstructed attractors of model-generated and measured data are the same. Training is stopped when the prediction error is low and the model passes this test. Two other features of the algorithm are (1) the way the state of the system, consisting of delays from the time series, has its dimension reduced by weighted principal component analysis data reduction, and (2) the user-adjustable prediction horizon obtained by "error propagation"-partially propagating prediction errors to the next time step. The algorithm is first applied to data from an experimental-driven chaotic pendulum, of which two of the three state variables are known. This is a comprehensive example that shows how well the Diks test can distinguish between slightly different attractors. Second, the algorithm is applied to the same problem, but now one of the two known state variables is ignored. Finally, we present a model for the laser data from the Santa Fe time-series competition (set A). It is the first model for these data that is not only useful for short-term predictions but also generates time series with similar chaotic characteristics as the measured data.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

Anti-synchronization of stochastic perturbed delayed chaotic neural networks

This paper studies the anti-synchronization of a class of stochastic perturbed chaotic delayed neural networks. By employing the Lyapunov functional method combined with the stochastic analysis as well as the feedback control technique, several sufficient conditions are established that guarantee the mean square exponential anti-synchronization of two identical delayed networks with stochastic disturbances. These sufficient conditions, which are expressed in terms of linear matrix inequalities (LMIs), can be solved efficiently by the LMI toolbox in Matlab. Two numerical examples are exploited to demonstrate the feasibility and applicability of the proposed synchronization approaches.     

Estimation of the dimension of chaotic dynamical systems using neural networks and robust location estimate

In this paper, we propose a new algorithm for the estimation of the dimension of chaotic dynamical systems using neural networks and robust location estimate.The basic idea is that a member of a time series can be optimally expressed as a deterministic function of the d past series values, where d is the dimension of the system. Moreover the neural networks’ learning ability is improved rapidly when the appropriate amount of information is provided to a neural structure which is as complex as needed.To estimate the dimension of a dynamical system, neural networks are trained to learn the component of the attractor expressed by a reconstructed vector in a suitable phase space whose embedding dimension m, has been estimated using the method of mutual information.     

混沌系统的混合遗传神经网络控制

在小扰动控制技术基础上,将暂态误差预测方法和遗传算法结合起来,提出了一种混合遗传神经网络控制非线性混沌系统的新方法(简称HyGANN).通过增强学习训练,HyGANN可产生控制混沌状态的小扰动时间序列信号,Henon映射的计算机仿真结果表明,它不仅有效镇定混沌周期1,2等低周期轨道,还可成功将高周期混轨道变成期望周期行为.     

Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks

We propose to fit a recurrent feedback neural network structure to input–output data through prediction error minimization. The recurrent feedback neural network structure takes the form of a nonlinear state estimator, which can compactly represent a multivariable dynamic system with stochastic inputs. The inclusion of the feedback error term as an input to the model allows the user to update the model based on feedback measurements in real-time uses. The model can be useful in a variety of applications including software sensing, process monitoring, and predictive control. A dynamic learning algorithm for training the recurrent neural network has been developed. Through some examples, we evaluate the efficacy of the proposed method and the prediction improvement achieved by the inclusion of the feedback error term.     

Adaptive synchronization of different kinds of chaotic neural networks

The purpose of the paper is to present an adaptive control method for the synchronization of different classes of chaotic neural networks. A new sufficient condition for the global synchronization of two kinds of chaotic neural networks is derived. The proposed control method is efficient for implementing the synchronization when the parameters of the drive system are different from those of the response system. A numerical example is used to demonstrate the validity of the proposed method and the obtained result.     

Adaptive synchronization of different kinds of chaotic neural networks

The purpose of the paper is to present an adaptive control method for the synchronization of different classes of chaotic neural networks. A new sufficient condition for the global synchronization of two kinds of chaotic neural networks is derived. The proposed control method is efficient for implementing the synchronization when the parameters of the drive system are different from those of the response system. A numerical example is used to demonstrate the validity of the proposed method and the obtained result.     

Global impulsive exponential anti-synchronization of delayed chaotic neural networks

In this paper, the impulsive exponential anti-synchronization for chaotic delayed neural networks is investigated. By establishing an integral delay inequality and using the inequality method, some sufficient conditions ensuring impulsive exponential anti-synchronization of two chaotic delayed networks are derived. To illustrate the effectiveness of the new scheme, a numerical example is given.     

A method for analyzing the spatiotemporal changes of chaotic neural networks

A chaotic neural network proposed (CNN) by Aihara et al. is able to recollect stored patterns dynamically. But there are difficult cases such as its long time processing of association, and difficult to recall a specific stored pattern during the dynamical associations. We have proposed to find the optimal parameters using meta-heuristics methods to improve association performance, for example, the shorter recalling time and higher recollection rates of stored patterns in our previous works. However, the relationship between the different values of parameters of chaotic neurons and the association performance of CNN was not investigated clearly. In this paper, we propose a method to analyze the spatiotemporal changes of internal states in CNN and, by the method, analyze how the change of values of internal parameters of chaotic neurons affects the characteristics of chaotic neurons when multiple patterns are stored in the CNN. Quantile–Quantile plot, least square approximation, hierarchical clustering, and Hilbert transform are used to investigate the similarity of internal states of chaotic neurons, and to classify the neurons. Simulation results showed that how different values of an internal parameter yielded different behaviors of chaotic neurons and it suggests the optimal parameter which generates higher association performance may concern with the stored patterns of the CNN.     

On the exponential synchronization of stochastic impulsive chaotic delayed neural networks

In this paper, the exponential synchronization of stochastic impulsive chaotic delayed neural networks is investigated. Based on the Lyapunov function method, time-varying delay feedback control technique and the efficient modified Halanay inequality for stochastic differential equations, several sufficient conditions are presented to guarantee the exponential synchronization in mean square between two identical chaotic delayed neural networks with stochastic and impulsive perturbations. These conditions are expressed in terms of linear matrix inequalities (LMIs), which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Comparing with the existing works that consider single perturbation (stochastic or impulsive one), the proposed method can provide a more general framework for the synchronization of multi-perturbation chaotic systems, which is favorable for practical application in secure communication. Finally, numerical simulations verify the effectiveness of the proposed method.     

混沌系统的RBF神经网络非线性补偿控制

设计RBF神经网络非线性补偿控制器,提出了混沌系统线性状态反馈的复合控制方法,将可调系统混沌行为镇定到期望目标位置或者变成周期运动.用Lorenz方程作仿真实验,结果证明了该方法的有效性.     

Neuron-synapse IC chip-set for large-scale chaotic neural networks.

We propose a neuron-synapse integrated circuit (IC) chip-set for large-scale chaotic neural networks. We use switched-capacitor (SC) circuit techniques to implement a three-internal-state transiently-chaotic neural network model. The SC chaotic neuron chip faithfully reproduces complex chaotic dynamics in real numbers through continuous state variables of the analog circuitry. We can digitally control most of the model parameters by means of programmable capacitive arrays embedded in the SC chaotic neuron chip. Since the output of the neuron is transfered into a digital pulse according to the all-or-nothing property of an axon, we design a synapse chip with digital circuits. We propose a memory-based synapse circuit architecture to achieve a rapid calculation of a vast number of weighted summations. Both of the SC neuron and the digital synapse circuits have been fabricated as IC forms. We have tested these IC chips extensively, and confirmed the functions and performance of the chip-set. The proposed neuron-synapse IC chip-set makes it possible to construct a scalable and reconfigurable large-scale chaotic neural network with 10000 neurons and 10000/sup 2/ synaptic connections.     

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.     

Dynamic characteristic of a multiple chaotic neural network and its application

Based on chaotic neural network, a multiple chaotic neural network algorithm combining two different chaotic dynamics sources in each neuron is proposed. With the effect of self-feedback connection and non-linear delay connection weight, the new algorithm can contain more powerful chaotic dynamics to search the solution domain globally in the beginning searching period. By analyzing the dynamic characteristic and the influence of cooling schedule in simulated annealing, a flexible parameter tuning strategy being able to promote chaotic dynamics convergence quickly is introduced into our algorithm. We show the effectiveness of the new algorithm in two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a maximum clique problem.