(1963). Cybernetics, or Control and Communication in the Animal and the Machine. Technometrics: Vol. 5, No. 1, pp. 128-130.

Cybernetics, or Control and Communication in the Animal and the Machine

As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel, which is, of course, not always the best choice. In this paper, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel, and present some important properties. We further propose the generalized maximum correntropy criterion (GMCC) and apply it to adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is derived, and the stability problem and steady-state performance are studied. We show that the proposed algorithm is very stable and can achieve zero probability of divergence (POD). Simulation results confirm the theoretical expectations and demonstrate the desirable performance of the new algorithm.

Generalized Correntropy for Robust Adaptive Filtering

Maximum correntropy Kalman filter

Effective connectivity: Influence, causality and biophysical modeling

The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.

Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research provides a base for the book, but it incorporates the results from the latest international research publications. One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developmentsContains numerous illustrative examples to help the reader grasp basic methods

System Parameter Identification: Information Criteria and Algorithms

Recently, the minimum error entropy (MEE) criterion has been used as an information theoretic alternative to traditional mean-square error criterion in supervised learning systems. MEE yields nonquadratic, nonconvex performance surface even for adaptive linear neuron (ADALINE) training, which complicates the theoretical analysis of the method. In this paper, we develop a unified approach for mean-square convergence analysis for ADALINE training under MEE criterion. The weight update equation is formulated in the form of block-data. Based on a block version of energy conservation relation, and under several assumptions, we carry out the mean-square convergence analysis of this class of adaptation algorithm, including mean-square stability, mean-square evolution (transient behavior) and the mean-square steady-state performance. Simulation experimental results agree with the theoretical predictions very well.

Mean-Square Convergence Analysis of ADALINE Training With Minimum Error Entropy Criterion

Recently, the minimum error entropy (MEE) cri- terion has been used as an information theoretic alternative to traditional mean-square error criterion in supervised learning systems. MEE yields nonquadratic, nonconvex performance sur- face even for adaptive linear neuron (ADALINE) training, which complicates the theoretical analysis of the method. In this paper, we develop a unified approach for mean-square convergence analysis for ADALINE training under MEE criterion. The weight update equation is formulated in the form of block-data. Based on a block version of energy conservation relation, and under several assumptions, we carry out the mean-square convergence analysis of this class of adaptation algorithm, including mean-square stability, mean-square evolution (transient behavior) and the mean-square steady-state performance. Simulation experimental results agree with the theoretical predictions very well.

Mean-Square Convergence Analysis of ADALINE Training With Minimum Error

The most important characteristic of a passive vibration isolator is its natural frequency and load capacity. The vibration isolation performance of a passive vibration isolator in low input frequencies is considerably improved by reducing its natural frequency. This paper presents a negative stiffness magnetic suspension vibration isolator (NSMSVI) using a magnetic spring combined with rubber membranes to obtain lower natural frequency. To study the vibration isolation performance of the NSMSVI, the stretching force of the rubber membrane is measured through experiments. The stiffness of the rubber membrane comes from the derivative of the stretching force. A parametric study of load capacity, axial magnetic stiffness, and natural frequency of the magnetic spring is also performed. Consequently, as a case study, the size dimensions of the magnetic spring are determined. An NSMSVI table is set up for experimental validation, after which the transmissibility curves of the NSMSVI are calculated and tested. Experimental results show that the lowest natural frequency of the NSMSVI reaches 1.5 Hz with a maximum attenuation of -40 dB between 0 Hz and 100 Hz, whereas the NSMSVI has the maximum load capacity at the lowest stiffness. This paper essentially provides an efficient method to construct a negative stiffness vibration isolator for practical applications.

A negative stiffness vibration isolator using magnetic spring combined with rubber membrane

This paper presents a new 2-D permanent-magnet array for a planar motor, in which the angle between the magnetization directions of any two adjacent magnets is 45°. The harmonic model for flux density distribution of the array is solved by the scalar magnetic potential equation and validated by the finite-element method. An analytical model for real-time control is derived by taking the first harmonic of the magnetic flux density distribution. The ignored higher harmonics in z-component of the magnetic flux density distribution is minimized by the genetic algorithm such that the analytical model becomes more accurate. Compared with the well-known Halbach magnet array, the proposed magnet array has lower higher harmonic components and higher z -component of the magnetic flux density, which will reduce the force ripples of the planar motor.

Jinchun Hu

Papers

System Parameter Identification: Information Criteria and Algorithms

Mean-Square Convergence Analysis of ADALINE Training With Minimum Error Entropy Criterion

Mean-Square Convergence Analysis of ADALINE Training With Minimum Error

A negative stiffness vibration isolator using magnetic spring combined with rubber membrane

Analysis and Optimization of a New 2-D Magnet Array for Planar Motor