Approximate reasoning approach to pattern recognition

Ray, Kumar Sankar; Ghoshal, Jayati

doi:10.1016/0165-0114(95)00095-x

Cited by 15 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normalized magnitude to [0,1] of the melody with musical notes of solfeggio is shown in Figure 9a; the train of square pulses (37) are shown in Figure 9b; the train of spikes based on the Augmented Spiking Neuron Models (41) and (42) are shown in Figure 9c; and in Figure 9d the recognized pattern of the syllable SI with methods ( 43) -( 48) is shown.…”

Section: Results Of the Augmented Spiking Neuron Model Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

Ramírez-Mendoza

2023

Mathematics

View full text Add to dashboard Cite

This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step activation function (STEPAF)-SPKAF, Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF, achieving very good results.

show abstract

Section: Results Of the Augmented Spiking Neuron Model Methodsmentioning

confidence: 99%

“…Other methods have been applied to pattern recognition, for example, the approximate reasoning method for pattern recognition, which consists of fuzzy implications and a composition inference rule [37].…”

Section: Fan-stepaf-spkaf Model For Syllable-based Speech Recognition...mentioning

confidence: 99%

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

Ramírez-Mendoza

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…(14) and the error E p i s using Eq. (15). Let E r j be the jth minimum error over all the strings i.e.…”

Section: 1mentioning

confidence: 99%

“…In the design of the classifier [15][16][17][18][19][20][21][22] we have basically two phases, namely the learning phase (training phase) where we estimate the fuzzy relation i (based on the algorithm of Sec. 3.2) for set of one-dimensional fuzzy implications (i.e.…”

Section: Designing Of the Classifier Based On The Fuzzy Relational Camentioning

confidence: 99%

Pattern Recognition Based on Fuzzy Set and Genetic Algorithm

Ray

2014

Int. J. Image Grap.

Self Cite

View full text Add to dashboard Cite

In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation R i between the antecedent clause and consequent clause of each one-dimensional fuzzy implication. For the estimation of R i we use floating point representation of GA. Thus a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non-fuzzy features of a test pattern can be classified. The performance of the proposed scheme is tested on synthetic data. Subsequently, we use the proposed scheme for the vowel classification problem of an Indian language. Finally, a benchmark of performance is established by considering multiplayer perception (MLP), support vector machine (SVM) and the present method. The Abalone, Hosse Colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. This new tool for pattern classification is very effective for classification of patterns under vegue and imprecise environment. It can provide multiple classification under overlapped classes.

show abstract

“…The FRM of the winning rule is traditionally used in the specialized literature [5][6][7][8][9]. It uses the maximum as aggregation function [10,11] to obtain the global information.…”

Section: Introductionmentioning

confidence: 99%

Using the Choquet Integral in the Fuzzy Reasoning Method of Fuzzy Rule-Based Classification Systems

Barrenechea

Bustince

Fernández

et al. 2013

Axioms

View full text Add to dashboard Cite

In this paper we present a new fuzzy reasoning method in which the Choquet integral is used as aggregation function. In this manner, we can take into account the interaction among the rules of the system. For this reason, we consider several fuzzy measures, since it is a key point on the subsequent success of the Choquet integral, and we apply the new method with the same fuzzy measure for all the classes. However, the relationship among the set of rules of each class can be different and therefore the best fuzzy measure can change depending on the class. Consequently, we propose a learning method by means of a genetic algorithm in which the most suitable fuzzy measure for each class is computed. From the obtained results it is shown that our new proposal allows the performance of the classical fuzzy reasoning methods of the winning rule and additive combination to be enhanced whenever the fuzzy measure is appropriate for the tackled problem.

show abstract

Approximate reasoning approach to pattern recognition

Cited by 15 publications

References 4 publications

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

Pattern Recognition Based on Fuzzy Set and Genetic Algorithm

Using the Choquet Integral in the Fuzzy Reasoning Method of Fuzzy Rule-Based Classification Systems

Contact Info

Product

Resources

About