Neurofuzzy modeling of chemical vapor deposition processes

The modeling of semiconductor manufacturing processes has been the subject of intensive research efforts for years. Physical-based (first-principle) models have been shown to be difficult to develop for processes such as plasma etching and plasma deposition, which exhibit highly nonlinear and complex multidimensional relationships between input and output process variables. As a result, many researchers have turned to empirical techniques to model many semiconductor processes. This paper presents a neurofuzzy approach as a general tool for modeling chemical vapor deposition (CVD) processes. A five-layer feedforward neural network is proposed to model the input-output relationships of a plasma-enhanced CVD deposition of a SiN film. The proposed five-layer network is constructed from a set of input-output training data using unsupervised and supervised neural learning techniques. Product space data clustering is used to perform the partitioning of the input and output spaces. Fuzzy logic rules that describe the input-output relationships are then determined using competitive learning algorithms. Finally, the fuzzy membership functions of the input and output variables are optimally adjusted using the backpropagation learning algorithm. A salient feature of the proposed neurofuzzy network is that after the training process, the internal units are transparent to the user, and the input-output relationship of the CVD process can be described linguistically in terms of IF-THEN fuzzy rules. Computer simulations are conducted to verify the validity and the performance of the proposed neurofuzzy network for modeling CVD processes     

Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control

The paper describes an approach to generating optimal adaptive fuzzy neural models from I/O data. This approach combines structure and parameter identification of Takagi-Sugeno-Kang (TSK) fuzzy models. We propose to achieve structure determination via a combination of modified mountain clustering (MMC) algorithm, recursive least squares estimation (RLSE), and group method of data handling (GMDH). Parameter adjustment is achieved by training the initial TSK model using the algorithm of an adaptive network based fuzzy inference system (ANFIS), which employs backpropagation (BP) and RLSE. Further, a procedure for generating locally optimal model structures is suggested. The structure optimization procedure is composed of two phases: 1) locally optimal rule premise variables subsets (LOPVS) are identified using MMC, GMDH, and a search tree (ST); and 2) locally optimal numbers of model rules (LONOR) are determined using MMC/RLSE along with parallel simulation mean square error (PSMSE) as a performance index. The effectiveness of the proposed approach is verified by a variety of simulation examples. The examples include modeling of a nonlinear dynamical process from I/O data and modeling nonlinear components of dynamical plants, followed by tracking control based on a model reference adaptive scheme (MRAC). Simulation results show that this approach is fast and accurate and leads to several optimal models     

A CVD epitaxial deposition in a vertical barrel reactor: processmodeling using cluster-based fuzzy logic models

A chemical vapor deposition (CVD) epitaxial deposition process modeling using fuzzy logic models (FLM's) has been proposed. The process modeling algorithm consists of a cluster estimation method and backpropagation algorithm to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation factor is used to obtain the optimum structure of the fuzzy model using the testing data. Upon the optimum structure being reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to a nonlinear function and a vertical chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models     

基于模糊聚类的改进模糊辨识方法

针对以往模糊建模方法不能很好优化模糊模型输入空间的问题,本文提出了一种基于新的目标函数的模糊聚类方法,从而使模型的输入输出空间映射空间具有逼近实际输出的能力,从而达到优化模型结构的目的.仿真实例表明,该方法能够辨识非线性系统,能显著提高建模的精度.     

Fuzzy rules generation using new evolutionary algorithms combinedwith multilayer perceptrons

Based on evolutionary algorithms (EAs) and multilayer perceptrons (MLPs), a fuzzy rules generation method inclusive of two main learning stages is presented in this paper. In the primary stage, a new EA is developed to generate numerical control rules from input-output data without the help of experts, which increases the diversity of individuals to reduce the opportunities of falling into local optima. Every generated numerical rule is accumulated in a lookup table called a numerical-rule-based controller (NRC). In the secondary stage, both antecedent and consequent variables of the numerical rules are fuzzified by training MLPs with the backpropagation algorithm. All training data are directly derived from the NRC with simple manipulations. Consequently, a linguistic-rule-based controller (LRC) consisting of the generated fuzzy rules is completed. Two illustrative experiments are successfully made on the computer simulation and hardware implementation of the NRCs and LRCs of different types using the new EA combined with the MLPs. The experimental results reveal that the proposed EA-MLP MLP approach is efficient and effective to generate fuzzy rules which control nonlinearly dynamical systems exceedingly well     

基于聚类模糊神经网络的非线性电路故障诊断

提出了一种基于聚类算法和模糊神经网络的非线性模拟电路故障诊断方法。通过一个无监督的聚类算法自组织地确定模糊规则的数目并生成一个初始的故障诊断模糊规则库，构造了一类模糊神经网络，通过训练调整网络权值，使故障诊断模糊规则库的分类更加精确，并通过仿真实验验证了该方法的有效性。     

Adaptive robot control using neural networks

This paper studies the trajectory tracking problem to control the nonlinear dynamic model of a robot using neural networks. These controllers are based on learning from input-output measurements and not on parametric-model-based dynamics. Multilayer recurrent networks are used to estimate the dynamics of the system and the inverse dynamic model. The training is achieved using the backpropagation method. The minimization of the quadratic error is computed by a variable step gradient method. Another multilayer recurrent neural network is added to estimate the joint accelerations. The control process is applied to a two degree-of-freedom (DOF) SCARA robot using a DSP-based controller. Experimental results show the effectiveness of this approach. The tracking trajectory errors are very small and torques expected at manipulator joints are free of chattering.<>     

Model identification in rapid thermal processing systems

Of the various techniques for controlling the temperature in rapid thermal processing (RTP), model-based control has the greatest potential for attaining the best performance, when the model is accurate. Some system identification methods are introduced to help obtain more accurate models from measured input-output data. For the first identification method, techniques for estimating the parameters (time constant and gain) of a particular physics-based model are presented. For the other, it is shown how to use the input-output measurements to obtain a black-box (autoregressive exogenous) model of the RTP system, which turns out to have better predictive capability. For each problem, the theoretical derivation of the identification technique and assumptions on which it is based are summarized, and experimental results based on data collected from an RTP system are described. Studying the DC response using the identified model led to a reconfiguration of the chamber geometry of the existing RTP system to more effectively distribute the light energy from the lamps     

短语音说话人辨认的研究

针对短语音说话人辨认训练语料不充分的特点,对特征参数和GMM模型进行优化和改进,提出一种基于局部模糊PCA的GMM说话人辨认方法.该方法采用特征组合代替单一特征,以提高有效特征维数来弥补特征样本的不足,并用局部模糊PCA对组合特征进行有效降维,在对识别率影响很小的前提下,降低了系统的时空复杂度.本文还对GMM参数初始化...     

Supervised and unsupervised learning in radial basis functionclassifiers

The paper considers a number of strategies for training radial basis function (RBF) classifiers. A benchmark problem is constructed using ten-dimensional input patterns which have to be classified into one of three classes. The RBF networks are trained using a two-phase approach (unsupervised clustering for the first layer followed by supervised learning for the second layer), error backpropagation (supervised learning for both layers) and a hybrid approach. It is shown that RBF classifiers trained with error backpropagation give results almost identical to those obtained with a multilayer perceptron. Although networks trained with the two-phase approach give slightly worse classification results, it is argued that the hidden-layer representation of such networks is much more powerful, especially if it is encoded in the form of a Gaussian mixture model. During training, the number of subclusters present within the training database can be estimated: during testing, the activities in the hidden layer of the classification network can be used to assess the novelty of input patterns and thereby help to validate network outputs     

Neural network approach to land cover mapping

A pattern classification method is proposed for remote sensing data using neural networks. First, the authors apply the error backpropagation (BP) algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. In order to get stable and precise classification results, the training data set is selected based on geographical information and Kohonen's self-organizing feature map. Using the training data set and the error backpropagation algorithm, a layered neural network is trained such that the training patterns are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of LANDSAT TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method     

Fuzzy logic models for thermally based microelectronicmanufacturing processes

This paper presents fuzzy logic models (FLM) to simulate two thermally based microelectronic manufacturing processes: the “pool boiling” in vapor phase soldering and silicon deposition process in a horizontal chemical vapor deposition (CVD) reactor. After a brief discussion of the various input-output models, we present our general approach to the development of FLM's, followed by their application to the two case studies. For the pool boiling, experimental data are used to develop the fuzzy logic model. Results show that the FLM not only simulates the different regions of the pool boiling curve satisfactorily, but also faithfully represents the two transitions. For the CVD process, pseudo-analytical equations from Eversteyn's paper are used to generate data under simulated production conditions. Results show that the model can describe the process very well. The physico-fuzzy model, incorporating the physical understanding of the process, is shown to improve the model's extrapolation capability     

距离决策下的模糊聚类集成模型

模糊聚类是近年来使用的一类性能较为优越的聚类算法,但该类算法对初始聚类中心敏感且对边界样本的聚类结果不够准确。为了提高聚类准确性、稳定性,该文通过联合多个模糊聚类结果,提出一种距离决策下的模糊聚类集成模型。首先,利用模糊C均值(FCM)算法对数据样本进行多次聚类,得到相应的隶属度矩阵。然后,提出一种新的距离决策方法,充分利用得到的隶属度关系构建一个累积距离矩阵。最后,将距离矩阵引入密度峰值(DP)算法中,利用改进的DP算法进行聚类集成以获取最终聚类结果。在UCI机器学习库中选择9个数据集进行测试,实验结果表明,相比经典的聚类集成模型,该文提出的聚类集成模型效果更佳。     

A granular description of ECG signals

In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.     

On the application and design of artificial neural networks formotor fault detection. I

The general design considerations for feedforward artificial neural networks (ANNs) to perform motor fault detection are presented. A few noninvasive fault detection techniques are discussed, including the parameter estimation approach, human expert approach, and ANN approach. A brief overview of feedforward nets and the backpropagation training algorithm, along with its pseudocodes, is given. Some of the neural network design considerations such as network performance, network implementation, size of training data set, assignment of training parameter values, and stopping criteria are discussed. A fuzzy logic approach to configuring the network structure is presented     

Predictive model of a reduced surface field p-LDMOSFET using neural network

Due to complex dynamics, it has been extremely difficult to model high power devices. A predictive model is constructed by using a backpropagation neural network (BPNN). The BPNN was applied to predict electrical characteristics of a reduced surface field p-channel lateral double-diffused MOSFET. Drain–source currents for applied drain–source voltages were measured with a HP4156A. Prediction performance of BPNN model was optimized with variations in training factors. With respect to the reference models, the optimized models demonstrated considerably improved predictions. Model predictions were highly consistent with actual measurements. Further improvement was obtained by constructing a modular network comprising multiple BPNNs.     

基于模糊分组和监督聚类的RBF回归性能改进

为了提高RBF回归建模的精度,该文提出了一种基于模糊分组和监督聚类的RBF回归建模的新方法。基本思想是:首先利用监督聚类将训练样本模糊划分为若干子集,然后分别针对各个子集的样本分布情况进行RBF回归建模,最后利用加权组合得到最终的输出。实验表明,该方法对于目标模型的局部细节具有更好的逼近精度。     

基于目标分群与模糊匹配的态势评估技术

采用目标分群和部队编制模糊匹配技术实现了态势评估系统。介绍了数据融合发展概况与融合模型,在数据融合修正模型的基础上,提出了态势评估总体技术框架、功能模块和关键技术。介绍了目标分群处理流程,包括目标分群、群的分裂与合并,并进一步阐述了目标分群算法与模糊匹配算法。介绍了基于模糊匹配技术实现军事体系单元假设推理的方法,给出目标分群计算结果,说明了算法的有效性。     

Fuzzy computing for data mining

The study is devoted to linguistic data mining, an endeavor that exploits the concepts, constructs, and mechanisms of fuzzy set theory. The roles of information granules, information granulation, and the techniques therein are discussed in detail. Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets. We introduce unsupervised learning (clustering) where optimization is supported by the linguistic granules of context, thereby giving rise to so-called context-sensitive fuzzy clustering. The combination of neuro, evolutionary, and granular computing in the context of data mining is explored. Detailed numerical experiments using well-known datasets are also included and analyzed     

Lloyd clustering of Gauss mixture models for image compression and classification

Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximate a large class of interesting densities and the availability of algorithms such as the Baum–Welch or expectation-maximization (EM) algorithm for constructing the models based on observed data. We here consider a quantization approach to Gauss mixture design based on the information theoretic view of Gaussian sources as a “worst case” for robust signal compression. Results in high-rate quantization theory suggest distortion measures suitable for Lloyd clustering of Gaussian components based on a training set of data. The approach provides a Gauss mixture model and an associated Gauss mixture vector quantizer which is locally robust. We describe the quantizer mismatch distortion and its relation to other distortion measures including the traditional squared error, the Kullback–Leibler (relative entropy) and minimum discrimination information, and the log-likehood distortions. The resulting Lloyd clustering algorithm is demonstrated by applications to image vector quantization, texture classification, and North Atlantic pipeline image classification.