Neuro-Fuzzy Classifiers/Quantifiers for E-Nose Applications
نویسنده
چکیده
منابع مشابه
A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR
The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...
متن کاملImproving Naive Bayes Classifiers Using Neuro-Fuzzy Learning
Naive Bayes classifiers are a well-known and powerful type of classifiers that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying them can sometimes lead to poor classification performance. Another prominent type of classifiers are neuro-fuzzy classification systems, which derive (fuzzy) classifiers from dat...
متن کاملClassification of Rice Grain Varieties Using Two Artificial Neural Networks (mlp and Neuro-fuzzy)
Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological featu...
متن کاملPerformance of weighted radial basis function classifiers
This paper describes Weighted Radial Basis Functions, a neuro-fuzzy uni cation algorithm which mixes Perceptrons and Radial Basis Functions. The algorithm has been tested as a pattern classi er in practical applications. Its performance are compared against those of other neural classi ers. The proposed algorithm has performance comparable or better than other neural algorithms, although it can...
متن کاملCombining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability
In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting class...
متن کامل