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Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters...
This paper presents the development and design of two software tools for computational intelligence. The software tools include a graphical user interface for construction, edition and observation of the intelligent systems. The software tool are for interval type-2 fuzzy logic and modular neural networks. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2...
The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the...
High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly...
The paper describes the combination of two modeling strategies between the accelerated Levenberg-Marquardt algorithm (accelerated LMA) on neuro-fuzzy approach and fuzzy clustering algorithm C-Means that can be used to forecast financial stock market such as Jakarta Stock Indices (JCI) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The accelerated...
In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
Traditional recognition methods which mainly match object images with their skeleton couldnpsilat resolve well complex objectspsila recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of...
In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation,...
The NetFlix Prize is a research contest that will award $1 Million to the first group to improve NetFlixpsilas movie recommendation system by 10%. Contestants are given a dataset containing the movie rating histories of customers for movies. From this data, a processing scheme must be developed that can predict how a customer will rate a given movie on a scale of 1 to 5. An architecture is presented...
PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that...
This paper aims at presenting different strategies for the construction of beta basis function (BBF) fuzzy neural network. These strategies lead to the determination of the network architecture by determining the structure of the hidden layer and parameters of its centers based on data structure. For that, we use self organizing maps (SOM) clustering to construct a mapped structure of the real training...
The paper presents a generalization of the framework for assessment of predictive models uncertainty using machine learning techniques. Historical model errors which are mismatch between observed and predicted values are assumed to be indicators of total model uncertainty; it is measured in the form of prediction intervals, and comprises all sources of uncertainty including model structure, model...
This paper presents an investigation into prototype-based classifiers. Different methods have been proposed to deal with this problem. There are two main classes of prototype-selection algorithms. The first is merely selective, in which the resulting set of prototypes is formed by well-chosen samples from the training set. The second is known as the creative class of algorithms. This strategy creates...
This paper proposes a novel classification method for image retrieval using gradient-based fuzzy c-means with divergence measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that utilizes the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed...
Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These...
A new methodology for forecasting of time series is proposed. It is based on combination of two techniques: fuzzy transform and perception-based logical deduction on the basis of learned linguistic description.
Artificial neural networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a fuzzy BPN, consisting of a back-propagation neural network (BPN) and a fuzzy membership function which takes advantage of the ANNspsila nonlinear features and interval values instead of the shortcoming of ANNspsila single-point estimation. To employ the two...
The recording of symbolic data has become a common practice with the advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued...
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