Design of a two-stage fuzzy classification model

This paper proposes a new incremental learning approach to endow a Takagi-Sugeno-type fuzzy classification model with high generalization ability. The proposed fuzzy model is learned through incremental support vector machine (SVM) and margin-selected gradient descent learning and is called FM3. In this learning approach, training samples are fed incrementally one-by-one instead of all in one batch. The FM3 evolves from an empty rule set. A one-pass clustering algorithm is used to determine the number of rules and initial fuzzy sets in the rule antecedent part. An online incremental linear SVM is proposed to tune the rule consequent parameters to endow the FM3 with high generalization ability. The use of incremental instead of batch SVM enables the FM3 to handle online training problems with only one new sample available at a time. For antecedent parameter learning, a margin-selected gradient descent algorithm is proposed to prevent overtraining. Simulation results and comparisons with SVMs and fuzzy classifiers with different learning algorithms demonstrate the advantage of the FM3.

Classification of mixed pixel is the difficult point of remote sensing image. Fuzzy theory is the effective method of classification of mixed pixel. It can get kind result in classification of mixed pixel. The fuzzy theory is adopted in the classification of remote sensing images, in which each pixel is not only sorted to a single type, but also several feature types. Since one pixel is correlated to several feature type, the degree of correlation to each type is expressed as [0,1], and then the membership degree of the pixel to every features is to be calculated for classification. This paper has carried on systematic theory analysis and experiment to TM remote sensing image with the classification model of the fuzzy theory. A experiment has carried through at aiming to the TM remote sensing images of Fuxin City in 2006. A pre-processing to the remote sensing images is done firstly, by means of MNF (minimum noise fraction transformation) to separate the noise and the signal. Next, the pure pixels are pickup through the pixel purity index images using the end-member selection of pixel purity index (PPI) and the final end-member is determined. At last, the classifying to the mixed pixels is carry trough by using fuzzy classification model, so that the classification result is obtained, and the precision is to be analyzed.

In this paper, a genetic algorithm with adaptive probabilities of crossover and mutation is introduced to find near global optimum parameters for the Neuro-new fuzzy reasoning model (NNFRM). The parameters to be optimized are those of input membership functions, output membership functions and relation matrix. A fuzzy evaluation criterion is introduced to evaluate the different fuzzy models. This criterion stresses the fact that the fuzzy system must be comprehensible and transparent to the user. The performance of the proposed model is evaluated using a benchmark problem. Also, the generalization of the proposed model is compared to the feed forward neural network. It is shown that the proposed GNNFRM outperforms other modeling methods. The generalization of the proposed model is better than that of the feed forward neural network.

BackgroundThe sequence database searching has been the dominant method for peptide identification, in which a large number of peptide spectra generated from LC/MS/MS experiments are searched using a search engine against theoretical fragmentation spectra derived from a protein sequences database or a spectral library. Selecting trustworthy peptide spectrum matches (PSMs) remains a challenge.ResultsA novel scoring method named FC-Ranker is developed to assign a nonnegative weight to each target PSM based on the possibility of its being correct. Particularly, the scores of PSMs are updated by using a fuzzy SVM classification model and a fuzzy silhouette index iteratively. Trustworthy PSMs will be assigned high scores when the algorithm stops.ConclusionsOur experimental studies show that FC-Ranker outperforms other post-database search algorithms over a variety of datasets, and it can be extended to solve a general classification problem with uncertain labels.

A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

A new fuzzy logic model with a genetic algorithm is developed that overcomes some of the inherent uncertainties in the fish stock-recruitment process. This model is applied to stock-recruitment relationships for the Southeast Alaska pink salmon (Oncorhynchus gorbuscha) and the West Coast Vancouver Island Pacific herring (Clupea pallasi) stocks. In both examples, the annual mean sea surface temperature is used as an environmental intervention in the model. The fuzzy logic model provides the functional relationship between the number of fish spawners and the sea surface temperature that is used to reconstruct the historical fish recruitment time series and also to predict the number of fish that will recruit in the future. Globally optimized genetic learning algorithms are used to find the optimal values of the parameters for the fuzzy logic model. The results from this fuzzy logic model are compared with results from both a traditional Ricker stock-recruitment model and a recent artificial neural network model. These comparisons demonstrate the superior capability of the fuzzy logic model for addressing problems of uncertainty and vagueness in both the data and the stock-recruitment relationship. The fuzzy logic model approach is recommended as a useful addition to the analytical tools currently available for fish stock assessment and management.

Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview.- Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges.- Identifying flexible structured premises for mining concise fuzzy knowledge.- A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems.- Extracting linguistic fuzzy models from numerical data-AFRELI algorithm.- Constrained optimization of fuzzy decision trees.- A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data.- A Feature Ranking Algorithm for Fuzzy Modelling Problems.- Interpretability in multidimensional classification.- Interpretable semi-mechanistic fuzzy models by clustering, OLS and FIS model reduction.- Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complexity minimization.- Simplification and reduction of fuzzy rules.- Effect of rule representation in rule base reduction.- Singular value-based fuzzy reduction with relaxed normalization condition.- Interpretability, complexity, and modular structure of fuzzy systems.- Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy.- About the trade-off between accuracy and interpretability of Takagi-Sugeno models in the context of nonlinear time series forecasting.- Accurate, transparent and compact fuzzy models by multi-objective evolutionary algorithms.- Transparent fuzzy systems in modeling and control.- Uniform fuzzy partitions with cardinal splines and wavelets: getting interpretable linguistic fuzzy models.- Relating the theory of partitions in MV-logic to the design of interpretable fuzzy systems.- A formal model of interpretability of linguistic variables.- Expressing relevance and interpretability of rule-based systems.- Conciseness of fuzzy models.- Exact trade-off between approximation accuracy and interpretability: solving the saturation problem for certain FRBSs.- Interpretability improvement of RBF-based neurofuzzy systems using regularized learning.- Extracting fuzzy classification rules from fuzzy clusters on the basis of separating hyperplanes.

Efficient search for fuzzy models using genetic algorithm

Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods

As the resource competition and contentions in data center networks (DCNs) are frequent and to large degrees, this becomes the reason of the evolution of our concept. Hedera, JellyFish, UltraLow, HULL, DCTCP are some of the tools and algorithms which are available for the implementation of this concept. Many of the existed methodologies transact not up to the level, due to high time and space constraints, so this leaves a space for some contribution. Maintenance of the priority queue to manage the traffic and the congestion in network for data of all sizes is the basic concept to be implemented here. Priority queue contains each element having some priority confederated with it. We enforce priority queue to prioritize huge extent of data accessible in data centers. Thus, to enhance the data flow at the high congestion network of data centers proposed methodology put forwards an idea of easing the flow by maintaining the priority queue which is powered with fuzzy classification and decision tree models. The transmission delay at the real time are analyzed and the paths are assigned accordingly with the evaluation of the request matrix. Finally scheduling through recursive genetic algorithm is done and efficiently data is transmitted from the source to the destination.

This paper presents a novel fuzzy model identification method, which is based on Genetic Algorithms and Particle Swarm Optimization. The proposed method is compared to other existing strategies for identification of fuzzy systems and equivalent linear models. A wind turbine system is used to verify and validate the proposed strategy. For purposes of this work, it is assumed that the simulator of the plant represents the actual system that needs to be identified. Simulations are carried out in continuous time and data are acquired with fixed sample time to generate a black box model of the system, using different techniques of identification.

A new approach of GA-based type reduction of interval type-2 fuzzy model for nonlinear MIMO system: Application in methane oxidation process

Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.