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We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic...
We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in . This approach allows to incorporate a priori knowledge in the form of additional (unlabeled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this...
High computational burden in solving quadratic programming problem is a major obstacle when we apply model predictive control to industrial process. Recurrent neural networks offer a new quadratic programming optimization approach due to its parallel computational performance. In this paper, we present a new architecture of solving model predictive control (MPC) problem based on one layer recurrent...
This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural...
The ldquosemantic gaprdquo observed in content-based image retrieval (CBIR) has become a highly active research topic in last twenty years, and it is widely accepted that domain specification is one of the most effective methods of addressing this problem. However, along with the challenge of making a CBIR system specific to a particular domain comes the challenge of making those features object dependent...
This paper proposes an improved method to modeling the dynamic process of basic oxygen furnace (BOF) and the main idea is simplification. Aiming at the problem that it is usually difficult to build a precise endpoint dynamic model because of the high dimensional input variables which affect the final results - carbon content and temperature, this paper builds endpoint carbon content prediction model...
Traditional recognition methods which mainly match object images with their skeleton couldnpsilat resolve well complex objectspsila recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of...
The multiresolution analysis learning algorithm (MRAL) for neural networks is proposed to get a more precious model from the noisy data set, which based on Multiresolution Analysis (MRA) of the wavelet transformation and nondominated sorting genetic algorithm-II (NSGA-II). Several different scaled signals of the error function are used as the objections, and NSGA-II algorithm is applied to optimize...
The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behavior. Artificial neural networks, trained using particle swarm optimization, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute...
This paper attempts to model human brainpsilas cognitive process at the primary visual cortex to comprehend road sign. The cortical maps in visual cortex have been widely focused in recent research. We propose a visual model that locates road sign in an image and identifies the localized road sign. Gabor wavelets are used to encode visual information and extract features. Self-organizing maps are...
This paper presents a modified version of U-tree (A.K. McCallum, 1996), a memory-based reinforcement learning (RL) algorithm that uses selective perception and short-term memory to handle partially observable Markovian decision processes (POMDP). Conventional RL algorithms rely on a set of pre-defined states to model the environment, even though it can learn the state transitions from experience....
In this paper, we propose a knowledge processing system using Kohonen feature map associative memory with refractoriness based on area representation. The proposed system is based on the Kohonen feature map associative memory with refractoriness based on area representation. In the conventional Kohonen feature map associative memory, only one-to-one associations can be realized. In contrast, one-to-many...
In this research, we propose a similarity-based image retrieval from plural key images by self-organizing map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the map layer corresponding to the input can fire sequentially because of the refractoriness. The proposed image retrieval system from plural key images using the self-organizing map with refractoriness...
In this paper, we propose a Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the Kohonen feature map associative memory with area representation for sequential patterns. Although the conventional Kohonen feature map associative memory with area representation for sequential patterns can deal with only binary (bipolar) patterns,...
In this research, we propose a similarity-based melody retrieval by self-organizing map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the map layer corresponding to the input can fire sequentially because of the refractoriness. The proposed melody retrieval system using the self-organizing map with refractoriness makes use of this property in order to retrieve...
This study theoretically analyzes and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared....
The novel chaotic stream DS-UWB system proposed in this paper accomplishes synchronization, modulation and encryption of data in only one channel transmission mechanism. The architecture of the system combines the chaotic pulse position modulation, the complex chaotic stream ciphers encryption and the chaotic direct spread codes with the PAM based DS-UWB communication system. The synchronization of...
In this paper, we discuss the shape of error surfaces, which represent error depending on parameters, in Spiking Neural Networks for SpikeProp. SpikeProp is a learning algorithm that adjusts timing of spikes. The discussion is held in the viewpoint of the difference between analogue computation and digital computation (especially in discrete time). Since the error is defined by timing of spikes, quantization...
Hypercolumn model (HCM) is a neural network model previously proposed to solve image recognition problem. In this paper, we propose an improved version of HCM network and demonstrate its ability to solve face recognition problem. HCM network is a hierarchical model based on self-organizing map (SOM) that closely follows the organization of visual cortex and builds an increasingly complex and invariant...
Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image...
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