220 research outputs found

    Nonlinear dynamics of full-range CNNs with time-varying delays and variable coefficients

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    In the article, the dynamical behaviours of the full-range cellular neural networks (FRCNNs) with variable coefficients and time-varying delays are considered. Firstly, the improved model of the FRCNNs is proposed, and the existence and uniqueness of the solution are studied by means of differential inclusions and set-valued analysis. Secondly, by using the Hardy inequality, the matrix analysis, and the Lyapunov functional method, we get some criteria for achieving the globally exponential stability (GES). Finally, some examples are provided to verify the correctness of the theoretical results

    State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

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    This paper is concerned with the exponential state estimation problem for a class of discrete-time fuzzy cellular neural networks with mixed time delays. The main purpose is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. By constructing a novel Lyapunov-Krasovskii functional which contains a triple summation term, some sufficient conditions are derived to guarantee the existence of the state estimator. The linear matrix inequality approach is employed for the first time to deal with the fuzzy cellular neural networks in the discrete-time case. Compared with the present conditions in the form of M-matrix, the results obtained in this paper are less conservative and can be checked readily by the MATLAB toolbox. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed results

    Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations

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    In this paper, finite-time synchronization of neural networks (NNs) with discontinuous activation functions (DAFs), Markovian switching, and proportional delays is studied in the framework of Filippov solution. Since proportional delay is unbounded and different from infinite-time distributed delay and classical finite-time analytical techniques are not applicable anymore, new 1-norm analytical techniques are developed. Controllers with and without the sign function are designed to overcome the effects of the uncertainties induced by Filippov solutions and further synchronize the considered NNs in a finite time. By designing new Lyapunov functionals and using M-matrix method, sufficient conditions are derived to guarantee that the considered NNs realize synchronization in a settling time without introducing any free parameters. It is shown that, though the proportional delay can be unbounded, complete synchronization can still be realized, and the settling time can be explicitly estimated. Moreover, it is discovered that controllers with sign function can reduce the control gains, while controllers without the sign function can overcome chattering phenomenon. Finally, numerical simulations are given to show the effectiveness of theoretical results

    Stability analysis for periodic solutions of fuzzy shunting inhibitory CNNs with delays

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    https://advancesindifferenceequations.springeropen.com/articles/10.1186/s13662-019-2321-z#rightslinkWe consider fuzzy shunting inhibitory cellular neural networks (FSICNNs) with time-varying coefficients and constant delays. By virtue of continuation theorem of coincidence degree theory and Cauchy–Schwartz inequality, we prove the existence of periodic solutions for FSICNNs. Furthermore, by employing a suitable Lyapunov functional we establish sufficient criteria which ensure global exponential stability of the periodic solutions. Numerical simulations that support the theoretical discussions are depicted

    Recent Advances and Applications of Fractional-Order Neural Networks

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    This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed

    Neuromorphic audio processing through real-time embedded spiking neural networks.

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    In this work novel speech recognition and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound classification, including Deep Learning techniques. Based on these studies, novel audio processing and automatic audio signal recognition systems are proposed, using a bio-inspired auditory sensor as input. A desktop software tool called NAVIS (Neuromorphic Auditory VIsualizer) for post-processing the information obtained from spiking cochleae was implemented, allowing to analyze these data for further research. Next, using a 4-chip SpiNNaker hardware platform and Spiking Neural Networks, a system is proposed for classifying different time-independent audio signals, making use of a Neuromorphic Auditory Sensor and frequency studies obtained with NAVIS. To prove the robustness and analyze the limitations of the system, the input audios were disturbed, simulating extreme noisy environments. Deep Learning mechanisms, particularly Convolutional Neural Networks, are trained and used to differentiate between healthy persons and pathological patients by detecting murmurs from heart recordings after integrating the spike information from the signals using a neuromorphic auditory sensor. Finally, a similar approach is used to train Spiking Convolutional Neural Networks for speech recognition tasks. A novel SCNN architecture for timedependent signals classification is proposed, using a buffered layer that adapts the information from a real-time input domain to a static domain. The system was deployed on a 48-chip SpiNNaker platform. Finally, the performance and efficiency of these systems were evaluated, obtaining conclusions and proposing improvements for future works.Premio Extraordinario de Doctorado U

    THERAPEUTIC VIDEO GAMES AND THE SIMULATION OF EXECUTIVE FUNCTION DEFICITS IN ADHD

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    Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulty paying attention, impulsivity, and hyperactivity. Diagnosis of ADHD rose 42% from 2003–2004 to 2011–2012. In 2011, 3.5 million children were treated with drugs. Optimizing therapy can take a year, and may not be completely effective. A clinical trial is currently being conducted of a device/drug combination using the computer game Minecraft, to determine how certain activities affect executive function, working memory, and restraint in patients diagnosed with ADHD. The human subjects’ responses are being modeled using artificial neural networks (ANNs), an artificial intelligence method that can be utilized to interpret highly complex data. We propose using ANNs to optimize drug and Minecraft therapy for individual patients based on the initial NICHQ Vanderbilt assessment scores. We are applying ANNs in the development of computational models for executive function deficiencies in ADHD. These models will then be used to develop a therapeutic video game as a drug/device combination with stimulants for the treatment of ADHD symptoms in Fragile X Syndrome. As a first step towards the design of virtual subjects with executive function deficits, computational models of the core executive functions working memory and fluid intelligence were constructed. These models were combined to create healthy control and executive function-deficient virtual subjects, who performed a Time Management task simulation that required the use of their executive functions to complete. The preliminary working memory model utilized a convolutional neural network to identify handwritten digits from the MNIST dataset, and the fluid intelligence model utilized a basic recurrent neural network to produce sequences of integers in the range 1-9 that can be multiplied together to produce the number 12. A simplified Impulsivity function was also included in the virtual subject as a first step towards the future inclusion of the core executive function inhibition
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