本文介绍了国际互联网WWW和其最新核心技术之一的Java语言的现状和发展，提出了一种将JavaApplet与WWW有机结合起来的客户在线交谈系统的方案，并结合具体情况讲述该交互系统在Internet（尤其是WWW）中的改进与多方面应用。

从控制工程的角度，概括介绍了作为国际互联网的最新核心技术之一的Java语言的特点和发展，并具体结合人工神经网络的仿真与实现，以实际例子论述其在科学研究中将发挥的巨大效用和带来的新思想。

Motivated by handling joint physical limits, environmental obstacles and various performance indices, researchers have developed a general quadratic-programming (QP) formulation for the redundancy resolution of robot manipulators. Such a general QP formulation is subject to equality constraint, inequality constraint and bound constraint, simultaneously. Each of the constraints has interpretably physical meaning and utility. Motivated by the real-time solution to the robotic problems, dynamic system solvers in the form of recurrent neural networks (RNN) have been developed and employed. This is in light of their parallel-computing nature and hardware implementability. In this paper, we have reviewed five RNN models, which include state-of-the-art dual neural networks (DNN) and LVI-based primal-dual neural networks (LVI-PDNN). Based on the review of the design experience, this paper proposes the concept, requirement and possibility of developing a future recurrent neural network model for solving online QP problems in redundant robotics; i.e., a piecewiselinear primal neural network.

A recurrent neural network called the dual neural network is proposed in this Letter for solving the strictly convex quadratic programming problems. Compared to other recurrent neural networks, the proposed dual network with fewer neurons can solve quadratic programming problems subject to equality, inequality, and bound constraints. The dual neural network is shown to be globally exponentially convergent to optimal solutions of quadratic programming problems. In addition, compared to neural networks containing high-order nonlinear terms, the dynamic equation of the proposed dual neural network is piecewise linear, and the network architecture is thus much simpler. The global convergence behavior of the dual neural network is demonstrated by an illustrative numerical example. Ù 2002 Elsevier Science B.V. All rights reserved.

In this paper, for handling general minimum-effort inverse-kinematic problems, the nonuniqueness condition is investigated. A set of nonlinear equations and inequality is presented for online nonuniqueness-checking. The concept and utility of primal neural networks (NNs) are introduced in this context of dynamical inequalities and constraints. The proposed primal NN can handle well such a nonlinear online-checking problem in the form of a set of nonlinear equations and inequality. Numerical examples demonstrate the effectiveness and advantages of the primal NN approach.