Max—min神经网络的一种有效学习算法

本文在Ａ．Ｂｌａｎｃｏ等人的算法的基础上，提出了ｍａｘ－ｍｉｎ神经网络的一种改进了的反馈学习算法，严格证明了该算法的迭代收敛性，理论分析及实例计算结果均表明，本文算法具有算法简单，收敛速度快，输出误差小等显著特点。     

Ergodic theory and the Strong Law of Large Numbers on Riesz spaces

In [W.-C. Kuo, C.C.A. Labuschagne, B.A. Watson, Discrete-time stochastic processes on Riesz spaces, Indag. Math. (N.S.) 15 (3) (2004) 435-451], we introduced the concepts of conditional expectations, martingales and stopping times on Riesz spaces. Here we formulate and prove order theoretic analogues of the Birkhoff, Hopf and Wiener ergodic theorems and the Strong Law of Large Numbers on Riesz spaces (vector lattices).     

牛顿第二定律实验中的误差分析

本文是通过对加速度的实验值和理论值进行比较和分析，给出误差的合理范围。     

Modeling Language Evolution

We describe a model for the evolution of the languages used by the agents of a society. Our main result proves convergence of these languages to a common one under certain conditions. A few special cases are elaborated in more depth.     

Almost-sure path properties of fractional Brownian sheet

The almost sure sample function behavior of the vector-valued fractional Brownian sheet is investigated. In particular, the global and the local moduli of continuity of the sample functions are studied. These results give precise information about the continuity and the oscillation behavior of the sample functions.     

A law of the iterated logarithm for processes with independent increments

ＡＬＡＷＯＦＴＨＥＩＴＥＲＡＴＥＤＬＯＧＡＲＩＴＨＭＦＯＲＰＲＯＣＥＳＳＥＳＷＩＴＨＩＮＤＥＰＥＮＤＥＮＴＩＮＣＲＥＭＥＮＴＳＷＡＮＧＪＩＡＧＡＮＧ（汪嘉冈）（ＥａｓｔＣｈｉｎａＵｎｉｖｅｒｓｉｔｙｏｆＳｃｉｅｎｃｅ＆Ｔｅｃｈｎｏｌｏｇｙ,Ｓｈａｎｇ...     

A hybrid disturbance observer for delivery drone’s oscillation suppression

This paper proposes a new hybrid disturbance observer (DOB) to help suppress disturbance to the control systems. The proposed hybrid DOB consists of three main parts: (1) an actual system, (2) a simulated system, and (3) a learning filter that connects the actual and simulated systems. The simulated system aims to replicate the actual system response, where it leverages a neural network model to predict the input disturbance and generate the predicted system response. Such system response is used to generate a learning signal through a learning filter; this learning signal is then added to the feedforward loop of the estimation framework to enhance the disturbance estimate and its suppression performance for the actual system. The proposed hybrid DOB is designed to advance the standard DOB structure with a learning-based feedforward compensation. While the proposed method does not modify the baseline controller, it is well suited to systems whose baseline controllers are difficult or impossible to be changed. Considering the delivery drones are subject to oscillations when dropping payloads, experimental tests with multiple payload dropping scenarios have been conducted using both the hybrid and standard DOB, where the compared results validate the effectiveness and advantages of the proposed hybrid DOB.