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Nonlinear Dynamics

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High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

International journal of neural systems
Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the ...

Rich spectrum of neural field dynamics in the presence of short-term synaptic depression.

Physical review. E, Statistical, nonlinear, and soft matter physics
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred...

A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.

IEEE transactions on neural networks and learning systems
In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, th...

Regular graphs maximize the variability of random neural networks.

Physical review. E, Statistical, nonlinear, and soft matter physics
In this work we study the dynamics of systems composed of numerous interacting elements interconnected through a random weighted directed graph, such as models of random neural networks. We develop an original theoretical approach based on a combinat...

Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem.

Computational intelligence and neuroscience
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continu...

Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems.

IEEE transactions on neural networks and learning systems
This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust...

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

Computational intelligence and neuroscience
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heurist...

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots.

IEEE transactions on neural networks and learning systems
We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion perfor...

Asymptotic Stability of a Class of Neutral Delay Neuron System in a Critical Case.

IEEE transactions on neural networks and learning systems
In this brief, the asymptotic stability properties of a neutral delay neuron system are studied mainly in a critical case when the exponential stability is not possible. If a critical value of the coefficient in the neutral delay neuron system is con...

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Neural networks : the official journal of the International Neural Network Society
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE a...