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Wind power prediction is of great significance to the safe and stable operation of the power system. The key factor of wind power prediction is the selection of prediction model. This paper chooses support vector machine (SVM) as the wind power prediction model and applies an improved grid search method to optimize the parameters of C and g in SVM model. The model is able to predict the real-time...
Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the...
In this study, we investigate the use of a Fuzzy C-Means Clustering based Neural Network (FNN) classifier in problems of emotion classification. The proposed classifier model consists of three layers, namely, input, hidden and output layers. Here, fuzzy c-means clustering method, two types of polynomial and linear combination function are used as a kernel function in the input layer, the hidden layer...
In most real-world problems, we are dealing with large size datasets. Reducing the number of irrelevant/redundant features dramatically reduces the running time of a learning algorithm and leads to a more general concept. In this paper, realization of feature selection through NeuroEvolution of Augmenting Topologies (NEAT) [1] is investigated which aims to pick a subset of features that are relevant...
One of the disadvantages of using Artificial Neural Networks (ANNs) is their significant training time need, which scales with the complexity of the network and with the complexity of the problem that is needed to be solved. Radial Basis Function Neural Networks (RBFNNs) are neural networks that use the linear combination of radial basis functions, utilizing hybrid learning procedures which can solve...
This paper proposes a new learning algorithm based on the versatile elliptic basis function (VEBF) by considering only the most data distributions for automatic computing the appropriate width vector. In addition, the orthonormal basis and Linear Discriminant Analysis (LDA) technique are also applied to the proposed method for adjusting the directions of the hyperellipsoid in the network and improving...
Medical science is characterized by the correct diagnosis of a disease and its accurate classification to avoid complexities at treatment/medication stage. This is often accomplished by a physician based on experience without much signal processing aids. It is envisioned that a sophisticated and intelligent medical diagnostic/classification system may be helpful in making right decisions especially...
In order to overcome the problem existing in original speech recognition (e.g. noise interruption and private data loss), many researchers have investigated to deal with these problems. Electromyography (EMG) from the muscles producing speech was used to replace a voiced signal. Similarly, we aim to develop EMG speech recognition based on Thai language. Tone is the important characteristic of this...
This work investigates the impact of the analogue front-end design (pre-amplifier, filter and converter) on spike sorting performance in neural interfaces. By examining key design parameters including the signal-to-noise ratio, bandwidth, filter type/order, data converter resolution and sampling rate, their sensitivity to spike sorting accuracy is assessed. This is applied to commonly used spike sorting...
Geometric dilution of precision (GDOP) is a powerful, simple and widely used measure for assessing the effectiveness of potential measurements to specify the precision and accuracy of the data received from global positioning system (GPS) satellites. The most correct method to classify or approximate the GPS GDOP is to use inverse matrix on all the combinations and choosing the lowest one, but inversing...
Prediction of Dengue presents great challenge as the clinical symptoms overlaps with other conventional fever. Dengue viral infection has been reported in more than 100 countries, with total of 2.5 billion people. Testing of prognosis, the stages of dengue such as DF, Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) under conventional methods needs frequent blood test information. The...
Several machine learning techniques have been applied for finding multi-loci associations among Single Nucleotide Polymorphisms (SNPs) and a disease. In this paper it is investigated whether Self Organizing Maps (SOMs) can generate clusters associated with a disease based on the genetic patterns of subjects. A batch categorical SOM that can handle missing data was used on Genome Wide Association (GWA)...
Multi-valued Neuron with Periodic activation function (MVN-P) was proposed for solving classification problems. The boundaries between two distinct categories are precisely specified in MVN-P, which may cause slow convergence in learning or low classification accuracy in generalization. In this paper, we propose a revised model, MVN-PFT, in which a fuzzy tolerating buffer is provided around a boundary...
Clustering is a predominant data mining task which attempts to partition a group of unlabelled data instances into distinct clusters. The clusters so obtained will have maximum intra-cluster similarity and minimum inter-cluster similarity. Several clustering techniques have been proposed in literature, which includes stand-alone as well as ensemble clustering techniques. Most of them lack robustness...
In this document we present a methodology for movement pattern recognition from arm-forearm myoelectric signals, starting off from the design and implementation of an electromyography (EMG) instrumentation system, considering the Surface EMG for the Non Invasive Assessment of Muscles (SENIAM) rules. Signal processing and characterization techniques were applied using the pass-band Butter worth digital...
Emotional speech recognition (ESR) from the aspect of human-machine interaction (HCI) is a prerequisite for the framework of interacting partners within the HCI. This paper addresses the application of neural network (NN) in ESR. The performance of NN is tested using three different feature sets which are basis for ESR: prosodic features, spectral features and a set of their combination. The results...
Authorship attribution, namely determination of the author of a text, may become an extraordinarily complex and sensitive job due to its relatively difficult feature extraction phase and highly nonlinear nature. This paper proposes a classification tool using committee machines consisting of multilayered perceptron neural networks (MLP) to identify the author of a text. Each expert is an individual...
Nowadays credit rating is of paramount importance in our economic system. In this article a new approach on financial credit rating will be demonstrated. Unlike many classical credit rating methods, the power of this solution lies in the use of artificial intelligence, which is getting more and more prevalent in economics [J. Bozsik, Neural Fuzzy System for Default Forecasts, 11th International Symposium...
In GPS/INS navigation, when GPS signals interrupt for a long time, traditional methods such as Kaiman Filtering (KF) and some artificial intelligence models can't work well in positioning precisely. This paper put forward a GPS/INS navigation model based on Input-Delay Neural Network (IDNN). This model use the past and real-time INS position or speed as samples to lag input, and then moderate train...
The paper deals with application of hybrid neural network model consisting of OM-PCNN and Projective ART for multidimensional data clustering. This paper points out the impact of cluster center selection for parameter σ in the Projective ART, which affects the number of clusters and the clustering accuracy.
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