Existence and exponential stability of solutions for quaternion-valued delayed hopfield neural networks by ξ-Norms

© 2013 IEEE. Recently, with the development of quaternion applications, quaternion-valued neural networks (QVNNs) have been presented and studied by more and more scholars. In this paper, the existence, uniqueness and exponential stability criteria of solutions for the quaternion-valued delayed Hopfield neural networks (QVDHNNs) are mainly investigated by means of the definitions of ξ-norms. In order to construct a ξ-norm, QVDHNNs system are decomposed into four real-number systems according to Hamilton rules. Then, taking advantage of ξ-norms, inequality technique and Cauchy's test for convergence, time-invariant delays and time-varying delays are considered successively to derive ξ-exponential type sufficient conditions. Based on these, several corollaries about the existence, uniqueness and exponential stability of solutions are obtained. Finally, two numerical examples with time-invariant delays and time-varying delays are given respectively. Their simulated images illustrate the effectiveness of the main theoretical results

Exponential Stability Analysis of Mixed Delayed Quaternion-Valued Neural Networks Via Decomposed Approach

© 2013 IEEE. With the application of quaternion in technology, quaternion-valued neural networks (QVNNs) have attracted many scholars' attention in recent years. For the existing results, dynamical behavior is an important studying side. In this paper, we mainly research the existence, uniqueness and exponential stability criteria of solutions for the QVNNs with discrete time-varying delays and distributed delays by means of generalized 2-norm. In order to avoid the noncommutativity of quaternion multiplication, the QVDNN system is firstly decomposed into four real-number systems by Hamilton rules. Then, we obtain the sufficient criteria for the existence, uniqueness and exponential stability of solutions by special Lyapunov-type functional, Cauchy convergence principle and monotone function. Furthermore, several corollaries are derived from the main results. Finally, we give one numerical example and its simulated figures to illustrate the effectiveness of the obtained conclusion

Stability analysis for delayed quaternion-valued neural networks via nonlinear measure approach

In this paper, the existence and stability analysis of the quaternion-valued neural networks (QVNNs) with time delay are considered. Firstly, the QVNNs are equivalently transformed into four real-valued systems. Then, based on the Lyapunov theory, nonlinear measure approach, and inequality technique, some sufficient criteria are derived to ensure the existence and uniqueness of the equilibrium point as well as global stability of delayed QVNNs. In addition, the provided criteria are presented in the form of linear matrix inequality (LMI), which can be easily checked by LMI toolbox in MATLAB. Finally, two simulation examples are demonstrated to verify the effectiveness of obtained results. Moreover, the less conservatism of the obtained results is also showed by two comparison examples

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Wind Energy Harvesting and Conversion Systems: A Technical Review

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting

Self Adaptive Reinforcement Learning for High-Dimensional Stochastic Systems with Application to Robotic Control

A long standing goal in the field of artificial intelligence (AI) is to develop agents that can perceive richer problem space and effortlessly plan their activity in minimal duration. Several strides have been made towards this goal over the last few years due to simultaneous advances in compute power, optimized algorithms, and most importantly evident success of AI based machines in nearly every discipline. The progress has been especially rapid in area of reinforcement learning (RL) where computers can now plan-ahead their activities and outperform their human rivals in complex problem domains like chess or Go game. However, despite encouraging progress, most of the advances in RL-based planning still take place in deterministic context (e.g. constant grid size, known action sets, etc.) which does not adapts well to stochastic variations in problem domain. In this dissertation we develop techniques that enable self-adaptation of agent\u27s behavioral policy when exposed to variations in problem domain. In particular, first we introduce an initial model that loosely realizes problem domain\u27s characteristics. The domain characteristics are embedded into a common multi-modal embedding space set. The embedding space set then allows us to identify initial beliefs and establish prior distributions without being constrained to only finite collection of agent\u27s state-action-reward experiences to choose from. We describe a learning technique that adapts to variations in problem domain by retaining only salient features of preceding domains, and inferring posterior for newly introduced variation as direct perturbation to aggregated priors. Besides having theoretical guarantees, we demonstrate end-to-end solution by establishing FPGA-based recurrent neural network, that can change its synaptic architecture temporally, thus eliminating the need of maintaining dual networks. We argue that our hardware based neural implementation has practical benefits, due to the fact it only uses sparse network architecture and multiplex it on circuit level to exhibit recurrence, which can reduce inference latency on circuit-level, while maintaining equivalence to dense neural architecture

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Receding-horizon motion planning of quadrupedal robot locomotion

Quadrupedal robots are designed to offer efficient and robust mobility on uneven terrain. This thesis investigates combining numerical optimization and machine learning methods to achieve interpretable kinodynamic planning of natural and agile locomotion. The proposed algorithm, called Receding-Horizon Experience-Controlled Adaptive Legged Locomotion (RHECALL), uses nonlinear programming (NLP) with learned initialization to produce long-horizon, high-fidelity, terrain-aware, whole-body trajectories. RHECALL has been implemented and validated on the ANYbotics ANYmal B and C quadrupeds on complex terrain. The proposed optimal control problem formulation uses the single-rigid-body dynamics (SRBD) model and adopts a direct collocation transcription method which enables the discovery of aperiodic contact sequences. To generate reliable trajectories, we propose fast-to-compute analytical costs that leverage the discretization and terrain-dependent kinematic constraints. To extend the formulation to receding-horizon planning, we propose a segmentation approach with asynchronous centre of mass (COM) and end-effector timings and a heuristic initialization scheme which reuses the previous solution. We integrate real-time 2.5D perception data for online foothold selection. Additionally, we demonstrate that a learned stability criterion can be incorporated into the planning framework. To accelerate the convergence of the NLP solver to locally optimal solutions, we propose data-driven initialization schemes trained using supervised and unsupervised behaviour cloning. We demonstrate the computational advantage of the schemes and the ability to leverage latent space to reconstruct dynamic segments of plans which are several seconds long. Finally, in order to apply RHECALL to quadrupeds with significant leg inertias, we derive the more accurate lump leg single-rigid-body dynamics (LL-SRBD) and centroidal dynamics (CD) models and their first-order partial derivatives. To facilitate intuitive usage of costs, constraints and initializations, we parameterize these models by Euclidean-space variables. We show the models have the ability to shape rotational inertia of the robot which offers potential to further improve agility