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Studies on chaos synchronization in coupled chaotic circuits are extensively carried out in various fields. In this study, synchronization patterns generated in a ring of cross-coupled chaotic circuits are investigated. Computer simulations show that this coupled system produces several phase patterns.
We propose a relational neural network defined as a special instance of the recurrent cascade correlation. The proposed model is designed to deal with classification tasks where classes are organized into generic graphs (e.g. taxonomies, ontologies etc.). The open challenge is to exploit the knowledge encoded in the relationships among the classes. This is particularly useful when there are many classes...
The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual...
To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural network (ZNN) could globally exponentially converge to the exact inverse of a given time-varying matrix. To find out the effect of time-derivative term on global convergence as well as for easier hardware-implementation...
Sound localisation is defined as the ability to identify the position of a sound source. The brain employs two cues to achieve this functionality for the horizontal plane, interaural time difference (ITD) by means of neurons in the medial superior olive (MSO) and interaural intensity difference (IID) by neurons of the lateral superior olive (LSO), both located in the superior olivary complex of the...
In this work, we characterize genes using an oligonucleotide affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior distributions of the class-specific gene expressions...
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,...
Software reliability is one of the most important quality characteristics for almost all systems. The use of a software reliability model to estimate and predict the system reliability level is fundamental to ensure software quality. However, the selection of an appropriate model for a specific case can be very difficult for project managers. This is because, there are several models that can be used...
This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functions of the visual cortex. We propose a novel hierarchical model based on invariance and independent component analysis for spatiotemporal feature extraction. Training the model from patches sampled from natural scenes, we can...
Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ldquothe curse of dimensionalityrdquo. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components...
The ensemble paradigm for machine learning has been studied for more than two decades and many methods, techniques and algorithms have been developed, and increasingly used in various applications. Nevertheless, there are still some fundamental issues remaining to be addressed, and an important one is what factors affect the accuracy of an ensemble, and to what extent they do, which is thus taken...
A solution for the load balancing problem in local clusters of heterogeneous processors is proposed within the setting of delayed artificial neural networks, optimal control and linear matrix inequalities (LMI) theory. Based on a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of delayed cellular neural networks...
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...
Optimal active learning refers to a framework where the learner actively selects data points to be added to its training set in a statistically optimal way. Under the assumption of log-loss, optimal active learning can be implemented in a relatively simple and efficient manner for regression problems using Gaussian processes. However (to date), there has been little attempt to study the experimental...
Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more...
Knowledge on primary processing of sound by the human auditory system has tremendously increased. This paper exploits the opportunities this creates for assessing the impact of (unwanted) environmental noise on quality of life of people. In particular the effect of auditory attention in a multisource context is focused on. The typical application envisaged here is characterized by very long term exposure...
Computational fluid dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have...
This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume finite deformation of the brain after opening the skull. We use a test sphere as a model of the brain, tetrahedral finite element mesh, and function optimization that optimizes the modelspsila...
In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg-Marquardt...
This paper presents a method to interpret the output of a classification (or regression) model. The interpretation is based on two concepts: the variable importance and the value importance of the variable. Unlike most of the state of art interpretation methods, our approach allows the interpretation of the model output for every instance. Understanding the score given by a model for one instance...
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