2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)最新文献_第2页

A self-organising fuzzy neural network with locally recurrent self-adaptive synapses 具有局部循环自适应突触的自组织模糊神经网络

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945927

D. Coyle, G. Prasad, T. McGinnity

{"title":"A self-organising fuzzy neural network with locally recurrent self-adaptive synapses","authors":"D. Coyle, G. Prasad, T. McGinnity","doi":"10.1109/EAIS.2011.5945927","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945927","url":null,"abstract":"This paper describes a modification to the learning algorithm and architecture of the self-organizing fuzzy neural network (SOFNN) to improve learning ability. Previously the SOFNN's computational efficiency was improved using a new method of checking the network structure after it has been modified. Instead of testing the entire structure every time it has been modified, a record is kept of each neuron's firing strength for all data previously clustered by the network. This record is updated as training progresses and is used to reduce the computational load of checking network structure changes, to ensure performance degradation does not occur, resulting in significantly reduced training times. To exploit the temporal information contained in the record of saved firing strengths, a new architecture of the SOFNN is proposed in this paper where recurrent feedback connections are added to neurons in layer three of the structure. Recurrent connections allow the network to learn the temporal information from the data and, in contrast to pure feed forward architectures, which exhibit static input-output behavior in advance, recurrent models are able to store information from the past (e.g., past measurements of the time-series) and are therefore better suited to analyzing dynamic systems. Each recurrent feedback connection includes a weight which must be learned. In this work a learning approach is proposed where the recurrent feedback weight is updated online (not iteratively) and proportional to the aggregate firing activity of each fuzzy neuron. It is shown that this modification, which conforms to the requirements for autonomy and has no additional hyperparameters, can significantly improve the performance of the SOFNN's prediction capacity under certain constraints","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"13 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Incremental recursive fisher linear discriminant for online feature extraction 基于增量递归fisher线性判别法的在线特征提取

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945905

S. Ozawa, Ryohei Ohta

{"title":"Incremental recursive fisher linear discriminant for online feature extraction","authors":"S. Ozawa, Ryohei Ohta","doi":"10.1109/EAIS.2011.5945905","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945905","url":null,"abstract":"In this paper, we propose a new online feature extraction algorithm called Incremental Recursive Fisher Linear Discriminant (IRFLD). In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of a between-class covariance matrix. However, the proposed IRFLD can remove this limitation. That is, an arbitrary number of discriminant vectors up to input dimensions can be obtained to construct a feature space. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant space. In addition, a suitable number of effective discriminant vectors are automatically determined using a cross-validation method, where several representative training data are held as validation data and they are updated using the k-means clustering whenever a chunk of new training data are given. The performance of IRFLD is evaluated for 5 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary discriminant space.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121946229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

An adaptive spiking neural network with Hebbian learning 具有Hebbian学习的自适应尖峰神经网络

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945923

L. Long

引用次数: 4

Control system with evolving Gaussian process models 演化高斯过程模型控制系统

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945910

D. Petelin, J. Kocijan

{"title":"Control system with evolving Gaussian process models","authors":"D. Petelin, J. Kocijan","doi":"10.1109/EAIS.2011.5945910","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945910","url":null,"abstract":"Control system based on evolving Gaussian process (GP) models is an example of self-learning closed-loop control system. It is meant for closed-loop control of dynamic systems where not much prior knowledge exists or where systems dynamics varies with time or operating region. GP models are non-parametric black-box models which represent a new method for system identification. GP models differ from most other frequently used black-box identification approaches as they do not try to approximate the modeled system by fitting the parameters of the selected basis functions, but rather search for the relationships among measured data. While GP models are Bayesian models, their output is normal distribution, expressed in terms of mean and variance. Latter can be interpreted as a confidence in prediction and used in many fields, especially in control system. Successful control system needs as much as possible data about process to be controlled. If the prior knowledge about the system to be controlled is scarce or the system varies with time or operating region, this control problem can be solved with an iterative method which adapts model with information obtained with streaming data and concurrently optimizes hyperparameter values. While that kind of method for GP models does not yet exist, concepts for evolving GP models and control system based on evolving GP models are proposed in this paper. It is flexible approach within which various ways of model adaptations can be used. One of those possibilities is illustrated with a control of a benchmark problem.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115327070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Dynamic pattern recognition for the diagnosis of evolving systems 动态模式识别在演化系统诊断中的应用

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945913

S. Mazeghrane, Laurent Hartert, M. S. Mouchaweh

{"title":"Dynamic pattern recognition for the diagnosis of evolving systems","authors":"S. Mazeghrane, Laurent Hartert, M. S. Mouchaweh","doi":"10.1109/EAIS.2011.5945913","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945913","url":null,"abstract":"In this paper, we propose an approach to achieve the monitoring of the functioning (normal, faulty) of the Steam Generator (SG) of the nuclear Prototype Fast Reactor (PFR). This approach is based on three steps: signal analysis, clustering and classification. The first step analyzes the acoustic signals measuring the noises issued of the injection of water or Argon in the SG. These injections simulate a leakage representing a faulty functioning mode of the steam generator. The goal of the signal analysis is to determine the minimal set of parameters required to discriminate the normal and faulty modes in the feature space. In the clustering step, the patterns obtained by the acoustic signals analysis are labeled as belonging to the first class (non-injection) or to the second class (injection) corresponding respectively to normal and faulty functioning modes. Finally, the decision function is generated in the third step in order to assign a new pattern (new acoustic signal) to one of the two learned classes. We use the Semi-Supervised Dynamic Fuzzy K-Nearest Neighbours (SS-DFKNN) method to achieve the clustering and the online classification of the new incoming patterns.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124866398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Case studies with evolving fuzzy grammars 使用不断发展的模糊语法的案例研究

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945912

T. Martin, N. Sharef

引用次数: 2

Adaptive kernel smoothing regression using vector quantization 基于矢量量化的自适应核平滑回归

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945916

Federico Montesino-Pouzols, A. Lendasse

{"title":"Adaptive kernel smoothing regression using vector quantization","authors":"Federico Montesino-Pouzols, A. Lendasse","doi":"10.1109/EAIS.2011.5945916","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945916","url":null,"abstract":"A method for performing kernel smoothing regression in an online adaptive manner is presented. The approach proposed is to apply kernel smoothing regression on an incremental estimation of the (evolving) probability distribution of the incoming data stream rather than the sequence of observations. This is achieved by performing vector quantization on the incoming stream. In addition, the kernel bandwidth is adapted online using a criterion based on the median absolute deviation estimator which can be computed efficiently online. Thus, adaptive kernel smoothing regression is computed on an evolving density estimation. The method is fast and suitable for modeling streams of data. This approach is shown to be more accurate than standard kernel smoothing regression and faster for datasets larger than a few hundred observations. Experiments performed using zero order or Nadaraya-Watson kernel regression show competitive accuracy and speed of the method as compared with well-known methods for adaptive regression, such as multivariate adaptive offline regression splines (MARS), online regression, such as online-sequential extreme learning machine (OS-ELM), and evolving intelligent systems applied to regression problems, namely dynamic evolving neural-fuzzy inference system (DENFIS) and evolving Takagi-Sugeno (eTS).","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134451023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density 使用非参数先行词和相对数据密度简化模糊规则系统

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945926

P. Angelov, R. Yager

{"title":"Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density","authors":"P. Angelov, R. Yager","doi":"10.1109/EAIS.2011.5945926","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945926","url":null,"abstract":"In this paper a new method for definition of the antecedent/premise part of the fuzzy rule-based (FRB) systems is proposed. It removes the need to define the membership functions per variable using often artificial parametric functions such as triangular, Gaussian etc. Instead, it strictly follows the real data distribution and in this sense resembles particle filters. In addition, it is in a vector form and thus removes the need to use logical connectives such as AND/OR to aggregate the scalar variables. Finally, it uses the relative data density expressed in a form of a parameter-free (Cauchy type) kernel to derive the activation level of each rule; these are then fuzzily weighted to produce the overall output. This new simplified type of FRB can be seen as the next form after the two popular FRB system types, namely the Zadeh-Mamdani and Takagi-Sugeno. The new type of FRB has a much simplified antecedent part which is formed using data clouds. Data clouds are sets of data samples in the data space and differ from clusters significantly (they have no specific shape, boundaries, and parameters). An important specific of the activation level determined by relative density is that it takes directly into account the distance to all previous data samples, not just the mean or prototype as other methods do. The proposed simplified FRB types of systems can be applied to off-line, on-line as well as evolving (with adaptive system structure) versions of FRB and related neuro-fuzzy systems. They can also be applied to prediction, classification, and control problems. In this paper examples will be presented of an evolving FRB predictor and of a classifier of one rule per class type which will be compared with the traditional approaches primarily aiming proof of concept. More thorough investigation of the rich possibilities which this innovative technique offers will be presented in parallel publications.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116815098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 82

Re-usable features in a hierarchical concept network for autonomous learning in complex games 用于复杂博弈中自主学习的层次概念网络中的可重用特征

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945908

Anthony Knittel, T. Bossomaier

{"title":"Re-usable features in a hierarchical concept network for autonomous learning in complex games","authors":"Anthony Knittel, T. Bossomaier","doi":"10.1109/EAIS.2011.5945908","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945908","url":null,"abstract":"The use of re-usable features to define conceptual elements is a recognised trait in many models of semantic memory, and provides advantages in efficiency of representation, and a manner to preserve links between related concepts. In order to form scalable and generalisable representations, autonomous systems are advantaged by the ability to re-use features, and to develop such a network of features autonomously. Existing learning systems that build knowledge structures in a reinforcement based environment tend to use separately defined rules, rather than re-use of shared features. The system described is a form of Learning Classifier System, based on the Activation-Reinforcement Classifier System that reinforces rules according to separate properties of expected reward and accessibility. This provides a useful platform for examining the construction of rules from re-used features. An implementation is described that constructs a network of features, that are used to define rules. This is able to operate successfully on the game of Dots and Boxes, providing stable operation and the ability to activate rules from a body of 4000 autonomously developed features. Examining the network produced shows a scale-free connectivity distribution, which is a property common in human semantic networks.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123040329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Evolving fuzzy linear regression trees with feature selection 基于特征选择的模糊线性回归树进化

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945919

A. Lemos, W. Caminhas, F. Gomide

引用次数: 10