模糊聚类支持向量机在步态分类中的应用

背景：对于患有神经系统或骨骼肌肉系统疾病的患者,分析步态数据可以评定康复程度,制定治疗方案。如何有效地分类小样本步态数据成为重要的研究课题。 目的：用改进的支持向量机算法对小样本步态数据进行分类,准确诊断疾病。 方法：建立加入模糊C均值聚类的支持向量机算法,选用Gait Dynamics in Neuro-Degenerative Disease Data Base 40~59岁年龄段的6组数据,共720个样本数据,采用左摆间隔和左支撑间隔两维参数对步态数据建模。数据归一化后,通过模糊C均值聚类对数据进行预处理;然后用支持向量机对数据进行分类。采用不同核函数的支持向量机算法验证分类能力。 结果与结论：实验结果表明,利用改进的支持向量机算法,可以有效地对信号进行分类,有助于疾病的诊断和治疗方案的制定。     

RKF-PCA: Robust kernel fuzzy PCA

Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear components originates from the fact that PCA uses an affine transform defined by eigenvectors of the covariance matrix and the mean of the data.In this paper, a robust kernel PCA method that extends the kernel PCA and uses fuzzy memberships is introduced to tackle the two problems simultaneously. We first introduce an iterative method to find robust principal components, called Robust Fuzzy PCA (RF-PCA), which has a connection with robust statistics and entropy regularization. The RF-PCA method is then extended to a non-linear one, Robust Kernel Fuzzy PCA (RKF-PCA), using kernels. The modified kernel used in the RKF-PCA satisfies the Mercer’s condition, which means that the derivation of the K-PCA is also valid for the RKF-PCA. Formal analyses and experimental results suggest that the RKF-PCA is an efficient non-linear dimension reduction method and is more noise-robust than the original kernel PCA.     

Robust support vector regression in the primal

The classical support vector regressions (SVRs) are constructed based on convex loss functions. Since non-convex loss functions to a certain extent own superiority to convex ones in generalization performance and robustness, we propose a non-convex loss function for SVR, and then the concave-convex procedure is utilized to transform the non-convex optimization to convex one. In the following, a Newton-type optimization algorithm is developed to solve the proposed robust SVR in the primal, which can not only retain the sparseness of SVR but also oppress outliers in the training examples. The effectiveness, namely better generalization, is validated through experiments on synthetic and real-world benchmark data sets.     

Robust stability of stochastic fuzzy delayed neural networks with impulsive time window

The urgent problem of impulsive moments which cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, the novel concept of impulsive time window is proposed in this paper. And the stability problem of stochastic fuzzy uncertain delayed neural networks with impulsive time window is investigated. By combining the discretized Lyapunov function approach with mathematical induction method, several novel and easy-to-check sufficient conditions concerning the impulsive time window are derived to ensure that the model considered here is exponentially stable in mean square. Numerical simulations are presented to further demonstrate the effectiveness of the proposed stability criterion.     

A fuzzy rule-based system for epileptic seizure detection in intracranial EEG

ObjectiveWe present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy.MethodsWe designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts’ reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7 h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system.ResultsThe system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis.ConclusionThe fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns.SignificanceThis system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.     

模糊支持向量分类判别睡眠呼吸暂停综合征

背景：目前,睡眠呼吸暂停综合征的诊断主要依赖多导睡眠分析仪,该测量方法不但操作复杂、费用昂贵、分析耗时,而且在一定程度上影响患者的睡眠状况。目的：分析心率变异性与睡眠呼吸暂停综合征的关系,提出一种简便准确的睡眠呼吸暂停综合征的检测算法。方法：对38名健康者和28例不同程度睡眠呼吸暂停综合征患者的心率数据,采用去趋势波动分析法和自回归模型谱分析法,分析心率变异性与睡眠时相的相关性,并选取患者的性别、年龄以及心率变异性在各个睡眠阶段的标度指数及低频/高频比例作为睡眠呼吸暂停综合征初筛的特征参数,应用模糊支持向量机对睡眠呼吸暂停综合征阳性和阴性进行分类判别。结果与结论：实验结果表明,模糊支持向量机的分类正确率达到93.94%。与现有睡眠呼吸暂停综合征的诊断方法相比,该方法测量简单方便,具有较高的诊断准确率。     

Prediction of autistic disorder using neuro fuzzy system by applying ANN technique

The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise.     

Distributed semi-supervised support vector machines

The semi-supervised support vector machine (S${}^3$VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S${}^3$VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over networks, where communication is restricted only to neighboring agents and no coordinating authority is present. Using a standard relaxation of the original S${}^3$VM, we formulate the training problem as the distributed minimization of a non-convex social cost function. To find a (stationary) solution in a distributed manner, we employ two different strategies: (i) a distributed gradient descent algorithm; (ii) a recently developed framework for In-Network Nonconvex Optimization (NEXT), which is based on successive convexifications of the original problem, interleaved by state diffusion steps. Our experimental results show that the proposed distributed algorithms have comparable performance with respect to a centralized implementation, while highlighting the pros and cons of the proposed solutions. To the date, this is the first work that paves the way toward the broad field of distributed semi-supervised learning over networks.     

A computer support system for neurological anatomical diagnosis

Objectives

Accurate neurological diagnoses are often difficult to make due to the complexity of the neuroanatomy involved. This study was performed to evaluate the usefulness of a computer system with easily retrievable anatomical information as a support for arriving at more accurate anatomic diagnoses.

Patients and methods

Anatomical information from an initial physical examination was programmed into a computer with stored neuroanatomical charts of the brain, spinal cord and peripheral nerves. The information generated a graphic display of possible lesions with suggestions for further examination. These suggestions were then followed and further data entered. This data entry generated a new graphic display with reduced lesion possibilities. Iterations were then followed to narrow the possibilities for diagnosis further, until a final anatomical diagnosis was reached.This method was applied to three hypothetical examples and a number of clinical cases.Here we report three clinical cases in which this method was particularly useful in making a diagnosis.

Results

Using computer iterations, the system was able to pinpoint one or more presumptive causative lesions in the CNS or PNS based on known neuronal pathways or nuclei.

Conclusion

The results indicate that suitably used, computer memory, by virtue of its large capacity, accuracy and fast recall, can supplement human memory in reaching accurate anatomical diagnoses of neurological lesions.     

The POP learning algorithms: reducing work in identifying fuzzy rules

A novel fuzzy neural network, the Pseudo Outer-Product based Fuzzy Neural Network (POPFNN), and its two fuzzy-rule-identification algorithms are proposed in this paper. They are the Pseudo Outer-Product (POP) learning and the Lazy Pseudo Outer-Product (LazyPOP) learning algorithms. These two learning algorithms are used in POPFNN to identify relevant fuzzy rules. In contrast with other rule-learning algorithms, the proposed algorithms have many advantages, such as being fast, reliable, efficient, and easy to understand. POP learning is a simple one-pass learning algorithm. It essentially performs rule-selection. Hence, it suffers from the shortcoming of having to consider all the possible rules. The second algorithm, the LazyPOP learning algorithm, truly identifies the fuzzy rules which are relevant and does not use a rule-selection method whereby irrelevant fuzzy rules are eliminated from an initial rule set. In addition, it is able to adjust the structure of the fuzzy neural network. The proposed LazyPOP learning algorithm is able to delete invalid feature inputs according to the fuzzy rules that have been identified. Extensive experimental results and discussions are presented for a detailed analysis of the proposed algorithms.     

Storage and recall capabilities of fuzzy morphological associative memories with adjunction-based learning

We recently employed concepts of mathematical morphology to introduce fuzzy morphological associative memories (FMAMs), a broad class of fuzzy associative memories (FAMs). We observed that many well-known FAM models can be classified as belonging to the class of FMAMs. Moreover, we developed a general learning strategy for FMAMs using the concept of adjunction of mathematical morphology.In this paper, we describe the properties of FMAMs with adjunction-based learning. In particular, we characterize the recall phase of these models. Furthermore, we prove several theorems concerning the storage capacity, noise tolerance, fixed points, and convergence of auto-associative FMAMs. These theorems are corroborated by experimental results concerning the reconstruction of noisy images. Finally, we successfully employ FMAMs with adjunction-based learning in order to implement fuzzy rule-based systems in an application to a time-series prediction problem in industry.     

Categorization of novel tools by patients with Alzheimer's disease: Category-specific content and process

We examined the interaction of content and process in categorizing novel semantic material. We taught patients with Alzheimer's disease (AD) and healthy age-matched seniors a category of plausible novel tools by similarity- and rule-based processes, and compared the results with our previous parallel study of categorization of novel animals, in which AD patients were selectively impaired at rule-based categorization. AD patients demonstrated learning in the novel tool study; however, in contrast to the novel animal study, they were impaired in similarity-based as well as rule-based categorization relative to healthy seniors. Healthy seniors’ categorization strategies reflected process irrespective of category content; they frequently attended to a single feature following similarity-based training, and always attended to all requisite features following rule-based training. AD patients’ categorization strategies, in contrast, reflected category content; they frequently attended to a single feature when categorizing novel animals by either categorization process, but rarely did so when categorizing novel tools. AD patients’ ability to categorize novel tools correlated with preserved recognition memory, a pattern not found in the novel animal study. The category-specific role of memory, along with AD patients’ performance profile, suggests content-specific distinctions between the categories. We posit that tool features are relatively arbitrary, placing greater demands on memory, while prior knowledge about animals such as constraints on appearance and feature diagnosticity facilitates the assimilation of novel animals into semantic memory. The results suggest that categorization processes are sensitive to category content, which influences AD patients’ success at acquiring a new category.     

Automatic generation of fuzzy inference systems via unsupervised learning

In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs.     

Subspace-based support vector machines for pattern classification

In this paper, we discuss subspace-based support vector machines (SS–SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, for each class we define the weighted similarity measure using the vectors called dictionaries that represent the class, and optimize the weights so that the margin between classes is maximized. Because the similarity measure is defined for each class, for a data sample the similarity measure to which the data sample belongs needs to be the largest among all the similarity measures. Introducing slack variables, we define these constraints either by equality constraints or inequality constraints. As a result we obtain subspace-based least squares SVMs (SSLS–SVMs) and subspace-based linear programming SVMs (SSLP–SVMs). To speedup training of SSLS–SVMs, which are similar to LS–SVMs by all-at-once formulation, we also propose SSLS–SVMs by one-against-all formulation, which optimize each similarity measure separately. Using two-class problems, we clarify the difference of SSLS–SVMs and SSLP–SVMs and evaluate the effectiveness of the proposed methods over the conventional methods with equal weights and with weights equal to eigenvalues.     

A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept

The natural immune system provides an effective defense mechanism against foreign substances via complex interactions among various cells and molecules. Jerne introduced the immune network theory to model the relation between immune cells and molecules. The immune system like the neural system is able to learn from experience. In this paper, a multi-epitopic immune network model is proposed. The proposed model is hybridized with Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method. The new method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). To evaluate the performance of the proposed method several experiments on benchmark classification problems are carried out and the results are compared with two prominent immune-based classifiers as well as several versions of the LVQ algorithm. The results of the experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.     

Adaptive support vector regression for UAV flight control

This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.     

基于微量元素支持向量机在鼻咽癌模型预测中的应用*

背景：支持向量机目前已经在文本分类、手写识别、图像分类、生物信息学等诸多领域被成功应用。目的：采用智能算法,将支持向量机算法与微量元素数据结合对鼻咽癌患者建模,以提高鼻咽癌识别正确率。方法：基于微量元素数据,利用支持向量机对鼻咽癌患者、正常人、其他疾病患者样本建立分类模型。样品取自观察对象未染发头枕部紧贴头皮3 cm的头发。对样本进行的临床微量元素检测项目为6种元素锌、铜、铁、锰、镉、镍,加上年龄和性别共8项。采用高斯径向基函数为核函数、调节核函数参数C及σ以建立最佳支持向量机模型。结果与结论：采用十折交叉验证法得到模型的识别率分别为81.71%和66.47%。结果表明,基于微量元素的支持向量机法建立的鼻咽癌分类模型能较好的把鼻咽癌样本从正常人、各种疾病患者样本中区分出来。