The paper presents a multi-output wavelet neural network (WNN) which, taking benefit of wavelets and neural networks, is able to accomplish data feature extraction and modeling. In this work, WNN is implemented with a feedforward one-hidden layer architecture, whose activation functions in its hidden layer neurons are wavelet functions, in our case, the first derivative of a Gaussian function. The network training is performed using a backpropagation algorithm, adjusting the connection weights along with the network parameters. This principle is applied to the simultaneous quantification of heavy metals present in liquid media, taking the cyclic voltammogram obtained with a modified epoxy-graphite composite sensor as departure information. The combination between processing tools and electrochemical sensors is already known as an electronic tongue.
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture analysis such as segmentation plays a critical role in machine vision and pattern recognition applications. The widely applied areas are industrial automation, biomedical image processing and remote sensing. This paper describes a novel system for texture segmentation. We call this system Wavelet Oscillator Neural Networks (WONN). The proposed system is composed of two parts. A second-order statistical wavelet co-occurrence features are the first part of the proposed system and an oscillator neural network is in the second part of the system. The performance of the proposed system is tested on various texture mosaic images. The results of the proposed system are found to be satisfactory.
A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The ECG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and wavelet coefficients were calculated to represent the signals. The aim of the study is classification of the ECG beats by the combination of wavelet coefficients and PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features which well represent the ECG signals and the PNN trained on these features achieved high classification accuracies.
In the paper we deal with weak Boolean products of bounded dually residuated l-monoids (DRl-monoids). Since bounded DRl-monoids are a generalization of pseudo MValgebras and pseudo BL-algebras, the results can be immediately applied to these algebras.
In this paper the notion of weak chain-completeness is introduced for pseudo-ordered sets as an extension of the notion of chain-completeness of posets (see [3]) and it is shown that every isotone map of a weakly chain-complete pseudo-ordered set into itself has a least fixed point.
Some criteria for weak compactness of set valued integrals are given. Also we show some applications to the study of multimeasures on Banach spaces with the Radon-Nikodym property.
Here we consider the weak congruence lattice $C_{W}(A)$ of an algebra $A$ with the congruence extension property (the CEP for short) and the weak congruence intersection property (briefly the WCIP). In the first section we give necessary and sufficient conditions for the semimodularity of that lattice. In the second part we characterize algebras whose weak congruences form complemented lattices.
The full consistency of Saaty's matrix of preference intensities used in AHP is practically unachievable for a large number of objects being compared. There are many procedures and methods published in the literature that describe how to assess whether Saaty's matrix is "consistent enough". Consistency is in these cases measured for an already defined matrix (i.e. ex-post). In this paper we present a procedure that guarantees that an acceptable level of consistency of expert information concerning preferences will be achieved. The proposed method is based on dividing the process of inputting Saaty's matrix into two steps. First, the ordering of the compared objects with respect to their significance is determined using the pairwise comparison method. Second, the intensities of preferences are defined for the objects numbered in accordance with their ordering (resulting from the first step). In this paper the weak consistency of Saaty's matrix is defined, which is easy to check during the process of inputting the preference intensities. Several propositions concerning the properties of weakly consistent Saaty's matrices are proven in the paper. We show on an example that the weak consistency, which represents a very natural requirement on Saaty's matrix of preference intensities, is not achieved for some matrices, which are considered "consistent enough" according to the criteria published in the literature. The proposed method of setting Saaty's matrix of preference intensities was used in the model for determining scores for particular categories of artistic production, which is an integral part of the Registry of Artistic Results (RUV) currently being developed in the Czech Republic. The Registry contains data on works of art originating from creative activities of Czech art colleges and faculties. Based on the total scores achieved by these institutions, a part of the state budget subsidy is being allocated among them.