99,776 research outputs found
An analysis of possible applications of fuzzy set theory to the actuarial credibility theory
In this work, we review the basic concepts of actuarial credibility theory from the point of view of introducing applications of the fuzzy set-theoretic method. We show how the concept of actuarial credibility can be modeled through the fuzzy set membership functions and how fuzzy set methods, especially fuzzy pattern recognition, can provide an alternative tool for estimating credibility
Fuzzy Sets in Business Management, Finance, and Economics
This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences
Speech Recognition Using Combined Fuzzy and Ant Colony algorithm
In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm. First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals. Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Control chart patterns recognition using run rules and fuzzy classifiers considering limited data
Statistical process control chart is a common tool used for monitoring and detecting process variations. The process data streams, when graphically plotted on control chart reveal useful patterns. These patterns can be associated with possible assignable causes if properly recognized. These patterns detections are useful for process diagnostic. Different types of control chart pattern recognition methods are reported in literature. Most of the existing data-driven methods require a large amount for training data before putting into practice. Short production run and short product life cycle processes are usually constrained with limited data availability. Thus there is a need to investigate and develop an effective control chart pattern recogniser (CCPR) methods for process monitoring with limited data. Two methods were investigated in this study to recognize fully developed control chart patterns for process with limited data on X-bar chart. The first method was combination of selected run rules, as run rules do not require training data. Classifiers based on fuzzy set theory were the second method. The performance of these methods was evaluated based on percent correct recognition. The methods proposed in this study significantly reduced the requirements of training data. Different combination of Nelson’s run rules; R2,R5,R6 for shift and trend, R3,R5,R6 for cyclic, R4,R5,R8 for systematic and R7 for stratification patterns were found effective for recognizing. Differentiating between the shift and trend patterns remains challenging task for the run rules. Heuristic based Mamdani fuzzy classifier with fuzzy set simplification operations using statistical features gave more than ninety percent correct patterns recognition results. Adaptive neuro fuzzy inference system (ANFIS) fuzzy classifier with fuzzy c-mean using statistical features gave more prominent results. The findings suggest that the proposed methods can be used in short production run and the process with limited data. The fuzzy classifiers can be further studied for different input representation
Application of biosignal-driven intelligent systems for multifunction prosthesis control
University of Technology, Sydney. Faculty of Engineering and Information Technology.Prosthetic devices aim to provide an artificial alternative to missing limbs. The controller for such devices is usually driven by the biosignals generated by the human body, particularly Electromyogram (EMG) or Electroencephalogram (EEG) signals. Such a controller utilizes a pattern recognition approach to classify the EMG signal recorded from the human muscles or the EEG signal from the brain. The aim of this thesis is to improve the EMG and EEG pattern classification accuracy. Due to the fact that the success of pattern recognition based biosignal driven systems highly depends on the quality of extracted features, a number of novel, robust, hybrid and innovative methods are proposed to achieve better performance. These methods are developed to effectively tackle many of the limitations of existing systems, in particular feature representation and dimensionality reduction. A set of knowledge extraction methods that can accurately and rapidly identify the most important attributes for classifying the arm movements are formulated. This is accomplished through the following:
1. Developing a new feature extraction technique that can identify the most important features from the high-dimensional time-frequency representation of the multichannel EMG and EEG signals. For this task, an information content estimation method using fuzzy entropies and fuzzy mutual information is proposed to identify the optimal wravelet packet transform decomposition for classification.
2. Developing a powerful variable (feature or channel) selection paradigm to improve the performance of multi-channel EMG and EEG driven systems. This will eventually lead to the development of a combined channel and feature selection technique as one possible scheme for dimensionality reduction. Two novel feature selection methods are developed under this scheme utilizing the ant colony arid differential evolution optimization techniques. The differential evolution optimization technique is further modified in a novel attempt in employing a float optimizer for the combinatorial task of feature selection, proving powerful performance by both methods.
3. Developing two feature projection techniques that extract a small subset of highly informative discriminant features, thus acting as an alternative scheme for dimensionality reduction. The two methods represent novel variations to fuzzy discriminant analysis based projection techniques. In addition, an extension to the non-linear discriminant analysis is proposed based on a mixture of differential evolution and fuzzy discriminant analysis.
The testing and verification process of the proposed methods on different EMG and EEG datasets provides very encouraging results
Development of Fuzzy Hybrid Approaches to Project Delivery Method Selection in Highway Construction
Selection of project delivery methods is a success factor in delivering highway construction projects because it has a substantial impact on the project performance, such as cost, time, and quality. Project delivery decision-making processes have been heavily relied on experts’ opinions and subjective judgements of professionals to evaluate quantitative and qualitative decision variables. Although current quantitative and probabilistic methods provide a robust means to analyze quantitative variables, they are not ideally suited for treating uncertainties encountered in qualitative variables. Fuzzy set theory is a mathematical approach that can accommodate a combination of quantitative and qualitative variables. This dissertation aimed at investigating the applications of fuzzy set theory and fuzzy logic to support decision-making processes in project delivery method selections. Using an empirical dataset of 254 completed highway construction projects, three fuzzy-based applications, including fuzzy cluster analysis, fuzzy pattern recognition, and fuzzy Bayesian inference system were developed, trained, and tested. As a result, fuzzy cluster analysis was used to establish seven common project clusters that share high similarities in project characteristics, project complexity, delivery risks, cost growth, and project delivery methods. Fuzzy pattern recognition was used to develop a fuzzy rule-based inference system based on the seven identified project clusters to help recognize an appropriate project delivery method associated with potential cost growth for new highway projects. Fuzzy Bayesian networks were used to develop the theoretical framework of fuzzy Bayesian inference system which is able to depict the causal relationships between project characteristics, project complexity, delivery risks, and project delivery methods. The flexibility of fuzzy membership functions in the developed applications helps leverage the evaluation of a combination of quantitative and qualitative variables in highway project delivery method selection. In addition, these data-driven fuzzy applications also allow for multiple decision scenarios based on the decision maker’s judgements of delivery risks and project complexity. This dissertation contributes to the body of knowledge by demonstrating quantitative approaches derived from fuzzy set theory and fuzzy logic to support the selection of project delivery methods in highway construction. Additionally, the results from the developed fuzzy-based applications also provide insights regarding cost performance comparisons between project delivery methods. This study may assist highway agencies in making project delivery decisions based on project attributes, historical data and their relevant experience
Speaker independent isolated word recognition
The work presented in this thesis concerns the recognition of
isolated words using a pattern matching approach. In such a system,
an unknown speech utterance, which is to be identified, is
transformed into a pattern of characteristic features. These
features are then compared with a set of pre-stored reference
patterns that were generated from the vocabulary words. The unknown
word is identified as that vocabulary word for which the reference
pattern gives the best match.
One of the major difficul ties in the pattern comparison process is
that speech patterns, obtained from the same word, exhibit non-linear
temporal fluctuations and thus a high degree of redundancy. The
initial part of this thesis considers various dynamic time warping
techniques used for normalizing the temporal differences between
speech patterns. Redundancy removal methods are also considered, and
their effect on the recognition accuracy is assessed.
Although the use of dynamic time warping algorithms provide
considerable improvement in the accuracy of isolated word recognition
schemes, the performance is ultimately limited by their poor ability
to discriminate between acoustically similar words. Methods for
enhancing the identification rate among acoustically similar words,
by using common pattern features for similar sounding regions, are
investigated.
Pattern matching based, speaker independent systems, can only operate
with a high recognition rate, by using multiple reference patterns
for each of the words included in the vocabulary. These patterns are
obtained from the utterances of a group of speakers. The use of
multiple reference patterns, not only leads to a large increase in
the memory requirements of the recognizer, but also an increase in
the computational load. A recognition system is proposed in this
thesis, which overcomes these difficulties by (i) employing vector
quantization techniques to reduce the storage of reference patterns,
and (ii) eliminating the need for dynamic time warping which reduces
the computational complexity of the system.
Finally, a method of identifying the acoustic structure of an
utterance in terms of voiced, unvoiced, and silence segments by using
fuzzy set theory is proposed. The acoustic structure is then
employed to enhance the recognition accuracy of a conventional
isolated word recognizer
Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?
To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not
to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy
min-max neural network with its basic learning procedure is used within six different algorithm independent learning
schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation
of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on
unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four
criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability
to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning
scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions
examined is whether and when to use a single classifier or a combination of a number of component classifiers within a
multiple classifier system
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