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This paper presents a discrete-time neural network with a switching structure to solve a general quadratic programming problem in real time. Compared with existing ones for solving quadratic programming problems, the proposed neural network model has a simple architecture and uses a limited number of neurons to solve the problem, irrespective of the dimension of the decision variables or the number...
The proposed Fast Incremental Slow Feature Analysis (F-IncSFA) which is considered as unsupervised learning and it can be used for extracting the features. The featurescan represent the fundamental components of the modifications in different aspect and especially in posing and temporally firms and consistent even in high-dimensional input like signal, video, etc. Here, we addressed a development...
In this paper, the Self-Constructing Fuzzy Neural Network (SCFNN) controller suitable for real-time control of the speed control of the slide door is presented to track reference model. The structure and parameter learning can be done automatically and online. The structure learning is accordance with the partition of input space (error and change of error), and the parameter learning is based on...
Statistical approaches for Functional Data Analysis concern the paradigm for which the individuals are functions or curves rather than finite dimensional vectors. In this paper, we particularly focus on the modeling and the classification of functional data which are temporal curves presenting regime changes over time. More specifically, we propose a new mixture model-based discriminant analysis approach...
In this paper we compare the self organising capabilities of the Generative Topographic Map (GTM) [1] and Elastic Net (EN) [2]. We analytically compare the two algorithms and examine the different ways in which they preserve topography by considering their respective ‘state space trajectories’. We present simulations that demonstrate the differences between the two algorithms. We conclude by using...
A common approach when applying reinforcement learning to address control problems is that of first learning a policy based on an approximated model of the plant, whose behavior can be quickly and safely explored in simulation; and then implementing the obtained policy to control the actual plant. Here we follow this approach to learn to engage a transmission clutch, with the aim of obtaining a rapid...
With the advancement in technology, we see that complex-valued data arise in many practical applications, specially in signal and image processing. In this paper, we introduce a new application by generating complex-valued dataset that represents various hand gestures in complex domain. The system consists of three components: real time hand tracking, hand-skeleton construction, and hand gesture recognition...
In this paper, we present a complex-valued neuro-fuzzy inference system (CNFIS) and its gradient descent based learning algorithm developed employing Wirtinger calculus. The proposed CNFIS is a four layered network which realizes zero-order Takagi-Sugeno-Kang based fuzzy inference mechanism. CNFIS is used to predict the speed and direction of wind. Here, the speed and direction are considered as statistically...
Learning sparse representations for deep networks has drawn considerable research interest in recent years. In this paper, we present a novel framework to learn sparse representations via a generalized encoder-decoder architecture. The basic idea is to adopt a fast approximation to the iterative sparse coding solution and form an efficient nonlinear encoder to map an input to a sparse representation...
In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive...
Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach...
Chaotic neural networks are able to reproduce chaotic dynamics observable in the brain of various living beings. As a result, study of the dynamical properties of such networks may pave the way towards a better understanding of the memory rules of the brain. In this paper a simple neural circuit employing a theoretical memristive synapse with symmetric charge-flux nonlinearity is found to behave chaotically...
This paper proposes determinism measure (DET) based on recurrence plot, which is capable of showing the recurrence property of a deterministic dynamical system, to evaluate the dynamical characteristics of the surface electromyogram (sEMG) during three different hand movements. In addition, the linear discriminant analysis (LDA) is applied to evaluate the performance of the above measures to identify...
A recent study has shown that several gait-related artifact removal techniques are not helpful to improve the performances of an ultra-compact P300-based BCI system integrating only three electrodes and designed for out of the lab experiments. Moreover, the authors advise to use seven electrodes with a standard xDawn spatial filter - known to magnify the P300 response - in order to obtain the best...
The foremost objective of our research series is to construct a neurocomputational model that aims to achieve a Large-Scale Brain Network, and to suggest a possible insight of how the macro-level anatomical structures, such as the connectivity between the frontal lobe regions and their dynamic properties, can be self-organized to obtain the higher-order cognitive mechanisms, such as: planning, reasoning,...
Neural Networks are widely used to select features for classification / regression problems. These methods usually do not take into account the redundancy (linear/nonlinear dependency) between features. Consequently the selected set of features although useful, may contain redundant features. Here we propose a general framework for feature selection with controlled redundancy using a radial basis...
SpiNNaker is a hardware-based massively-parallel real-time universal neural network simulator designed to simulate large-scale spiking neural networks. Spikes are distributed across the system using a multicast packet router. Each packet represents an event (spike) generated by a neuron. On the basis of the source of the spike (chip, core and neuron), the routers distribute the network packet across...
Artificial Neural Networks (ANNs) has been applied in the face detection task because of its ability to capture the complex probability distribution conditioned to the class of face patterns. However, many works use Back-Propagation (BP) to adapt the weights of the ANNs. The problem of using BP is that it has many disadvantages related to the appropriate choice of its parameters, as the learning rate...
Maximum a posteriori (MAP)-based kernel classification trained by linear programming (MAPLP) has previously been proposed as a new approach to MAP-based classification. As opposed to conventional MAP-based approaches, MAPLP does not directly estimate a posteriori probabilities for classification, but instead introduces its surrogate function to an objective function that behaves similarly to a MAP-based...
Keyword extraction is vital for Knowledge Management System, Information Retrieval System, and Digital Libraries as well as for general browsing of the web. Keywords are often the basis of document processing methods such as clustering and retrieval since processing all the words in the document can be slow. Common models for automating the process of keyword extraction are usually done by using several...
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