An initial comparison of a fuzzy neural classifier and a decision tree based classifier

At the present time a large number of AI methods have been developed in the field of pattern classification. In this paper, we will compare the performance of a well-known algorithm in machine learning (C4.5) with a recently proposed algorithm in the fuzzy set community (NEFCLASS). We will compare the algorithms both on the accuracy attained and on the size of the induced rule base. Additionally, we will investigate how the selected algorithms perform after they have been pre-processed by discretization and feature selection.     

Neuron selection for RBF neural network classifier based on data structure preserving criterion

The central problem in training a radial basis function neural network is the selection of hidden layer neurons. In this paper, we propose to select hidden layer neurons based on data structure preserving criterion. Data structure denotes relative location of samples in the high-dimensional space. By preserving the data structure of samples including those that are close to separation boundaries between different classes, the neuron subset selected retains the separation margin underlying the full set of hidden layer neurons. As a direct result, the network obtained tends to generalize well.     

Evolving neural networks with iterative learning scheme for associative memory

A locally iterative learning (LIL) rule is adapted to a model of the associative memory based on the evolving recurrent-type neural networks composed of growing neurons. There exist extremely different scale parameters of time, the individual learning time and the generation in evolution. This model allows us definite investigation on the interaction between learning and evolution. And the reinforcement of the robustness against the noise is also achieved in the evolutional scheme.     

新的C-RBF神经网络分类器的设计与实现

从RBF神经元的几何意义出发,提出了一种新的用于模式识别的C-RBF神经网络分类器.与传统RBF网络相比,该算法能够自动地优化RBF网络中核函数的个数、中心和宽度,且由于竞争神经元的引入,省去了传统RBF神经网络输出层线性连接权的计算,从而简化了网络的学习过程,大大缩短了训练时间.     

Delay-derivative-dependent stability criterion for neural networks with probabilistic time-varying delay

In this article, based on Lyapunov–Krasovskii functional approach and improved delay-partitioning idea, a new sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic time-varying delay is addressed. Together with general convex combination method, the criterion is presented via LMIs and its solvability heavily depends on the sizes of both time delay range and its derivative, which has wider application fields than those present ones. It can be shown by the numerical examples that our method reduces the conservatism much more effectively than earlier reported ones. Especially, the conservatism can be further decreased by thinning the delay intervals.     

Windowed electroencephalographic signal classifier based on continuous neural networks with delays in the input

This study reports the design and implementation of a pattern recognition algorithm aimed to classify electroencephalographic (EEG) signals based on a class of dynamic neural networks (NN) described by time delay differential equations (TDNN). This kind of NN introduces the signal windowing process used in different pattern classification methods. The development of the classifier included a new set of learning laws that considered the impact of delayed information on the classifier structure. Both, the training and the validation processes were completely designed and evaluated in this study. The training method for this kind of NN was obtained by applying the Lyapunov theory stability analysis. The accuracy of training process was characterized in terms of the number of delays. A parallel structure (similar to an associative memory) with fixed (obtained after training) weights was used to execute the validation stage. Two methods were considered to validate the pattern classification method: a generalization-regularization and the k-fold cross validation processes (k = 5). Two different classes were considered: normal EEG and patients with previous confirmed neurological diagnosis. The first one contains the EEG signals from 100 healthy patients while the second contains information of epileptic seizures from the same number of patients. The pattern classification algorithm achieved a correct classification percentage of 92.12% using the information of the entire database. In comparison with similar pattern classification methods that considered the same database, the proposed CNN proved to achieve the same or even better correct classification results without pre-treating the EEG raw signal. This new type of classifier working in continuous time but using the delayed information of the input seems to be a reliable option to develop an accurate classification of windowed EEG signals.     

基于CMAC网络的迭代学习初始控制策略

针对传统迭代学习控制在面临新的环境或控制任务时学习时间长、收敛速度慢的问题,首先引入迭代学习初始控制算法,并给出了算法收敛的充分必要条件;然后,利用小脑模型连接控制网络(CMAC)与反馈PID网络进行综合,在系统的历史控制经验基础上,估计系统的期望控制输入,作为迭代学习控制器的初始控制输入,再由开闭环P型迭代学习律逐步改善控制效果,从而避免了对初始控制输入量的盲目选择,使得系统的实际输出只需较少的迭代次数就能达到跟踪的精度要求。机器人系统的仿真结果表明了该算法的可行性与有效性。     

Forecasting workload and airspace configuration with neural networks and tree search methods

The aim of the research presented in this paper is to forecast air traffic controller workload and required airspace configuration changes with enough lead time and with a good degree of realism. For this purpose, tree search methods were combined with a neural network.The neural network takes relevant air traffic complexity metrics as input and provides a workload indication (high, normal, or low) for any given air traffic control (ATC) sector. It was trained on historical data, i.e. archived sector operations, considering that ATC sectors made up of several airspace modules are usually split into several smaller sectors when the workload is excessive, or merged with other sectors when the workload is low. The input metrics are computed from the sector geometry and from simulated or real aircraft trajectories.The tree search methods explore all possible combinations of elementary airspace modules in order to build an optimal airspace partition where the workload is balanced as well as possible across the ATC sectors. The results are compared both to the real airspace configurations and to the forecast made by flow management operators in a French en-route air traffic control centre.     

Improved quasi-uniform stability criterion of fractional-order neural networks with discrete and distributed delays

This article mainly investigates the quasi-uniform stability of fractional-order neural networks with time discrete and distributed delays (FONNDDDs). First, a novel fractional-order Gronwall inequality with discrete and distributed delays (FOGIDDDs) is established; it can be used to study the stability of a variety of fractional-order systems with discrete and distributed delays (FOSDDDs). Second, on the basis of this inequality and Leray-Schauder alternative theorem, the existence and uniqueness results for the FONNDDDs are proved. Third, an improved criterion for the quasi-uniform stability of FONNDDDs is obtained in terms of this inequality. Ultimately, one numerical example is provided to expound the effectiveness and the superiority of the proposed result.     

Large tree classifier with heuristic search and global training

In the tree classifier with top-down search, a global decision is made via a series of local decisions. Although this approach gains in classification efficiency, it also gives rise to error accumulation which can be very harmful when the number of classes is very large. To overcome this difficulty, a new tree classifier with the following characteristics is proposed: 1) fuzzy logic search is used to find all ``possible correct classes,' and some similarity measures are used to determine the ``most probable class'; 2) global training is applied to generate extended terminals in order to enhance the recognition rate; 3) both the training and search algorithms have been given a lot of flexibility, to provide tradeoffs between error and rejection rates, and between the recognition rate and speed. A computer simulation of the decision trees for the recognition of 3200 Chinese character categories yielded a very high recognition rate of 99.93 percent and a very high speed of 861 samples/s, when the program was written in a high level language and run on a large multiuser time-sharing computer.     

New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay

This paper is concerned with the global asymptotic stability of a class of recurrent neural networks with interval time-varying delay. By constructing a suitable Lyapunov functional, a new criterion is established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and independent of the size of derivative of time varying delay. Two numerical examples show the effectiveness of the obtained results. Supported by the National Natural Science Foundation of China (Grant Nos. 60534010, 60728307, 60774048, 60774093), the Program for Cheung Kong Scholars and Innovative Research Groups of China (Grant No. 60521003) and the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z183), China Postdoctoral Sciencer Foundation (Grant No. 20080431150), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200801451096)     

Using LSTM neural networks for cross-lingual phonetic speech segmentation with an iterative correction procedure

This article describes experiments on speech segmentation using long short-term memory recurrent neural networks. The main part of the paper deals with multi-lingual and cross-lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi-lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well-tuned hidden Markov model-based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text-to-speech system. The generated speech quality was compared with the reference segmentation by a preference listening test.     

A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier

In this paper we propose a new static, global, supervised, incremental and bottom-up discretization algorithm based on coefficient of dispersion and skewness of data range. It automates the discretization process by introducing the number of intervals and stopping criterion. The results obtained using this discretization algorithm show that the discretization scheme generated by the algorithm almost has minimum number of intervals and requires smallest discretization time. The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by this algorithm. The efficiency of the proposed algorithm is shown in terms of better discretization scheme and better accuracy of classification by implementing it on six different real data sets.     

Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree

The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK’s problem.     

基于混合特征和分类树的细微表情识别方法

目前多数人脸表情识别的研究仅限于6种基本表情,未考虑到人脸表情变化是细微的.因此提出了基于混合特征和分类树的细微表情识别方法.对眼睛区域采用Gabor小波变换提取纹理变化特征,对鼻子区域采用2D-DCT提取纹理变化特征,而对嘴巴区域采用改进的AAM提取形状变化特征.分类识别时,将易混淆表情先归为一类进行表情的粗分类,然后对类内的表情选择相应表情贡献较大的特征子区域中的特征,进行表情细分类.在每级分类识别过程中,对每个区域采用离散HMM得出表情概率,最后采用在训练阶段得到的贡献权值进行加权融合得到分类结果.实验结果表明,该方法能够得到较好的识别效果,且处理速度快,适合于实时图像序列的细微表情识别.     

A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.     

基于PCS-PCA分类器和支持向量机的说话人确认

针对语音识别率不高的问题,提出一种基于PCS-PCA和支持向量机的分级说话人确认方法.首先采用主成分分析法对话者特征向量降维的同时,得到说话人特征向量的主成份空间,在此空间中构造PCS-PCA分类器,筛选可能的目标说话人,然后采用支持向量机进行最终的说话人确认.仿真实验结果表明该方法具有较高的识别率和较快的训练速度.     

Equilibria of iterative softmax and critical temperatures for intermittent search in self-organizing neural networks

Kwok and Smith (2005) recently proposed a new kind of optimization dynamics using self-organizing neural networks (SONN) driven by softmax weight renormalization. Such dynamics is capable of powerful intermittent search for high-quality solutions in difficult assignment optimization problems. However, the search is sensitive to temperature setting in the softmax renormalization step. It has been hypothesized that the optimal temperature setting corresponds to the symmetry-breaking bifurcation of equilibria of the renormalization step, when viewed as an autonomous dynamical system called iterative softmax (ISM). We rigorously analyze equilibria of ISM by determining their number, position, and stability types. It is shown that most fixed points exist in the neighborhood of the maximum entropy equilibrium w= (N(-1), N(-1), ..., N(-1)), where N is the ISM dimensionality. We calculate the exact rate of decrease in the number of ISM equilibria as one moves away from w. Bounds on temperatures guaranteeing different stability types of ISM equilibria are also derived. Moreover, we offer analytical approximations to the critical symmetry-breaking bifurcation temperatures that are in good agreement with those found by numerical investigations. So far, the critical temperatures have been determined only by trial-and-error numerical simulations. On a set of N-queens problems for a wide range of problem sizes N, the analytically determined critical temperatures predict the optimal working temperatures for SONN intermittent search very well. It is also shown that no intermittent search can exist in SONN for temperatures greater than one-half.     

An iterative learning scheme for motion control of robots using neural networks: A case study

In this paper, an iterative learning controller using neural networks has been studied for the motion control of robotic manipulators. Simulations of a two-link robot have demonstrated that the proposed control scheme for robotic manipulators can greatly reduce tracking errors after a few trials. Our modification of the original back-propagation algorithm is employed in the neural network, resulting in a much faster learning rate. The results of simulation have also shown that the proposed iterative learning controller has a faster rate of convergence and better robustness.     

基于模糊支持向量机与决策树的文本分类器

针对模糊支持向量机在文本分类应用中的隶属度函数确定问题,提出了一种基于模糊支持向量机与决策树的文本分类器的构建方法。该方法不仅考虑了样本与类中心之间的关系,还根据传统支持向量机中包含支持向量且平行于分类面的平面构建切球,来确定类中各个样本之间的关系,由样本点与球的位置关系计算其隶属度,可以合理地区分有效样本和噪音、孤立点样本。并与决策树方法相结合,实现多类分类。实验结果表明,该方法具有良好的分类效果。