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The paper is devoted to the problem of the active components (transistors) automated diagnostics on the base of his current-voltage characteristics and passive radio components (resistors) the prediction class by his parameters. The procedure of identification on the neuron nets base for these transistors is proposed. For automated diagnostic of passive radio components, for example, resistors, the...
A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. Reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This paper describes the method of hybridization and presents a model problem in which a...
Label-deficient semi-supervised learning is a challenging setting in which there is an abundance of unlabeled data but a dearth of labeled data. A hybrid network that mixes an autoencoder, capable of extracting information from unlabeled data, and a neural network classifier, which incorporates information from labeled data, can be useful in a label-deficient setting. In this case study, we examine...
Label-deficient semi-supervised learning is a challenging setting in which there is an abundance of unlabeled data but a dearth of labeled data. We propose a method for applying Gaussian process latent variable models (GPLVM) in a label-deficient setting, a method in which the discriminative GPLVM objective function trains a back-constraining neural network followed by a transformation into a semi-supervised...
A novel fMRI classification method designed for rapid event related fMRI experiments is described and applied to the classification of loud reading of isolated words in Hebrew. Three comparisons of different grammatical complexity were performed: (i) words versus asterisks (ii) “with diacritics versus without diacritics” and (iii) “with root versus no root”. We discuss the most difficult task and,...
In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and...
Stock trading investment is an important method for investors in financial market. Most researches focus on the precise price prediction; however, how to determine the trading points are more practical than getting the price predicted. This paper proposes a novel approach using Colored Petri Net to discover the relationships between various technical indicators, and exposing the trading rules of trading...
Learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-ELM-LRF) structure, while maintaining ELM's advantages of training efficiency. In this structure, ELM-LRF is firstly...
Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. Labels of bags are known but not those of individual instances...
Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model artificial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware...
It is well known that feed-forward neural networks can be learnt from symbolic data although the learnt networks usually have poor performance. This paper explores the ability of a recently popular feed-forward neural network, i.e., Extreme Learning Machine (ELM) for modeling symbolic data. An experimental study is conducted to compare C4.5 (a very popular algorithm of learning from symbolic data)...
Application of neural networks for direct prediction of lateral-directional force and moments coefficients from the measured flight data of the research aircraft is proposed in this paper. Proposed model of neural networks appears to be a suitable practical approach to develop relationship between flight variables. This relationship eliminates the need of aerodynamic model as well as thrust model...
The recognition of wine grapes in images acquired in natural environment is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red kinds is a well managed problem. On the other hand, the detection of white grapes is still a challenging task. In this contribution, the classifier for white wine grapes recognition is introduced and evaluated. The...
Face hallucination technique generates high-resolution face images from low-resolution ones. In this paper, we propose a patch based multitask deep learning method for face hallucination, which is robust to blurring of images. Our method is based on fully connected feedforward neural network, and the weights of the final layers are fine-tuned separately on different clusters of patches. Experimental...
In the application of Tropical Cyclone Track (TCT) forecast in South China Sea (SCS), pure linear neural network (PLNN) is used as the expert in the committee machine model, and it partly determines the model output. Data normalization is one of the most important factors, which affect the performance of the individual expert net. This paper aims to find how much data normalization affects the convergence...
Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation...
The process of knowledge discovery applied in distributed databases implies finding useful knowledge from mining data sets stored in real implementations of distributed databases. Distributed Databases represents a software system that allows a multitude of applications to access the data stored in local or remote databases. In this scenario, the data distribution is achieved through the process of...
The development of non-destructive methods like VIS-NIR reflection spectroscopy and artificial neural networks to analyse the rape seeds content of fat and protein was the subject of this work. The research material contained the seeds of 46 winter rapeseed lines obtained from interspecies crossing male sterile lines of MS-8 and 6 control forms. The seeds were pre-cleaned and crude fat and crude protein...
The Off-Grid systems are systems with an independence on the energy supply from external grid, whereas renewables (RES) are used as a sources of electric and heat energy. The main RES is photovoltaic power plant (PVP), however this source has the stochastic character of power supply. The stochastic character of PVP is given by dependency on a weather conditions. This brings a need of solar irradiance...
In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly...
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