The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a one-layer recurrent neural network for solving convex programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimization problem. Compared with the existing neural networks for optimization, the proposed neural network has lower model complexity. Moreover, the proposed...
A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is...
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...
Recently, a special kind of recurrent neural networks has been proposed by Zhang et al for online solution of Sylvester equation with time-varying coefficients. Their neural dynamics are elegantly introduced by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function, so that the computational error can vanish to zero globally and exponentially. The resultant...
Recently a sufficient condition for the recurrent neural network with the piecewise-linear output characteristic to generate a prescribed periodic sequence of binary vectors such that every two consecutive vectors differ in exactly one component has been derived. If a recurrent neural network satisfies this condition, it is guaranteed that any state trajectory of the network passes through the periodic...
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 presents some theoretical results on the global exponential stability of recurrent neural networks with pure time-varying delays. It is shown that the recurrent neural network is globally exponentially stable, if the pure time-varying delays satisfy some limitations. In addition to providing new criteria for recurrent neural networks with pure time varying delays, these stability conditions...
This paper presents the results of the models created for forecasting the hourly wind speed in 24-step-forward using Reservoir Computing (RC). RC is a new paradigm that offers an intuitive methodology for using the temporal processing power of recurrent neural networks (RNN) without the inconvenience of training them. Originally, introduced independently as Liquid State Machine [5] or Echo State Network...
This paper employs two types of neural networks to control a single-link flexible arm. To train each network, we utilize a gradient-based approach with adaptive learning rate. We first apply the diagonal recurrent neural network (DRNN) to a single-link flexible arm, which is a challenging control problem, in order to investigate the ability of this type of recurrent neural network. We then apply a...
As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed...
Distributed intelligent systems like self-organizing wireless sensor and actuator networks are supposed to work mostly autonomous even under changing environmental conditions. This requires robust and efficient self-learning capabilities implementable on embedded systems with limited memory and computational power. We present a new solution called spiral recurrent neural networks with an online learning...
Ambiguity in sensory-motor signals from a mobile robot due mainly to noise and fluctuation makes a deterministic approach unsatisfactory. In this paper, a stochastic approach based-on Hidden Markov Models (HMMs) is proposed to recognize environment of a mobile robot. From this recognition a graph-based map is formed. Graph-based maps are important in decreasing memory and the computational cost. Two...
A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received...
In this paper a new method to resolve the overfitting problem for predicting complex systemspsila behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.