A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators

The neural-network-based inverse kinematics solution is one of the recent topics in the robotics because of the fact that many traditional inverse kinematics problem solutions such as geometric, iterative and algebraic are inadequate for redundant robots. However, since the neural networks work with an acceptable error, the error at the end of inverse kinematics learning should be minimized. In this study, simulated annealing (SA) algorithm was used together with the neural-network-based inverse kinematics problem solution robots to minimize the error at the end effector. The solution method is applied to Stanford and Puma 560 six-joint robot models to show the efficiency. The proposed algorithm combines the characteristics of neural network and an optimization technique to obtain the best solution for the critical robotic applications. Three Elman neural networks were trained using separate training sets and different parameters, since one of them can give better results than the others can. The best result is selected within three neural network results by computing the end effector error via direct kinematics equation of the robotic manipulator. The decimal part of the neural network result was improved up to 10 digits using simulated annealing algorithm. The obtained best solution is given to the simulated annealing algorithm to find the best-fitting 10 digits for the decimal part of the solution. The end effector error was reduced significantly.     

Reliability-based approach to the inverse kinematics solution of robots using Elman's networks

The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.     

一种神经网络参数扰动算法及其在机械手控制中的应用

基于Ｈｏｐｆｉｅｌｄ型网络的收敛特性和Ｋｉｒｐａｔｒｉｃｋ等的模拟退火算法的思想，提出了一种克服Ｈｏｐｆｉｅｌｄ网络的局部极值问题的网络参数扰动算法，它具有类似ＳＡ算法的随机退火的特性。文中通过大量数字模拟分析了该算法的退火性能。     

基于混合神经网络与遗传算法方法的注塑参数优化

建立了基于混合神经网络与遗传算法方法的注塑工艺参数优化系统，用Matlab语言编制了应用程序，对神经网络的参数预测与遗传算法的优化过程进行求解。将网络预测结果与CAE模拟结果进行比较和误差分析，显示出BP网络的稳定性和可靠性；优化结果经CAE模拟和实验验证，证明是正确的，表明基于混合神经网络与遗传算法方法的注塑工艺参数优化方法是可行的。     

SA-HHGA优化RBF神经网络的方法及应用

提出一种利用模拟退火和混合递阶遗传算法优化RBF神经网络的方法。通过利用混合递阶遗传算法对RBF神经网络的拓扑结构、径向基中心和半径进行参数寻优,引入模拟退火算法对交叉和变异概率进行控制,采用最小二乘法确定网络的输出权值。将此方法应用于典型实例,并与其他四种方法进行对比,通过试验结果证明了该方法的准确率明显优于其他四种方法,方法的可行性和优越性得到验证。     

基于融合算法的测试优化选择问题研究

测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。     

基于SAGA-BP神经网络的证券智能分析系统研究

自然界中生物对复杂事物的处理机制在证券智能分析系统中应用,是提高证券预测分析准确性的一种有效途径。系统的运算功能模块分为两个方面:第一,它结合模拟退火算法和遗传算法的特点,有效的提高了处理复杂问题时算法收敛速度和精度;第二,它使用混合算法对神经网络的进行优化,充分发挥了神经网络的优点,弥补了它的缺点。根据需求对系统的功能模块进行分析设计,文章最后简要介绍MATLAB与Visual C++之间如何通信。     

PUMA机器人逆运动模拟退火粒子群求解方法

机器人逆运动问题随着运动关节的增多而越来越复杂,要建立逆运动通用的解析算法相当困难。提出利用模拟退火粒子群优化算法在解空间的搜索能力,直接从正向运动方程出发求解机器人关节变量的方法,讨论了目标函数的建立方式及算法实现步骤。实验分析该方法在位置和姿态方面的求解精度,并证实了算法的有效性。     

An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator

An adoptive learning strategy using an artificial neural network ANN has been proposed here to control the motion of a 6 D.O.F manipulator robot and to overcome the inverse kinematics problem, which are mainly singularities and uncertainties in arm configurations. In this approach a network have been trained to learn a desired set of joint angles positions from a given set of end effector positions, experimental results has shown an excellent mapping over the working area of the robot, to validate the ability of the designed network to make prediction and well generalization for any set of data, a new training using different data set has been performed using the same network, experimental results has shown a good generalization for the new data sets.The proposed control technique does not require any prior knowledge of the kinematics model of the system being controlled, the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify explicit robot system model. Any modification in the physical set-up of the robot such as the addition of a new tool would only require training for a new path without the need for any major system software modification, which is a significant advantage of using neural network technology.     

结合MPGA-RBFNN的一般机器人逆运动学求解

针对一般机器人逆运动学求解过程中存在的求解速度慢、精度低的问题，将多种群遗传算法（multiple population genetic algorithm，MPGA）引入径向基函数神经网络（radial basis functions neural network，RBFNN），提出一种适用于一般机器人的高精度MPGA-RBFNN算法。该算法采用3层结构的RBFNN进行一般机器人逆运动学求解，结合一般机器人的正运动学模型，采用MPGA优化RBFNN的网络结构和连接权值的方法，同时应用混合编码和演化的方式，实现了从机器人工作空间位姿到关节角度的非线性映射，从而避免了复杂的公式推导并提高了求解速度。采用6R一般机器人作为实验平台进行实验，实验结果表明:MPGA-RBFNN算法不仅提高了一般机器人在逆运动学中的求解速度，而且MPGA-RBFNN算法的训练成功率和逆解的计算准确率也得到了提高。     

基于免疫遗传算法的前向神经网络设计

针对传统BP算法训练速度慢、易陷入局部最优等缺点,该文提出了一种采用免疫遗传算法设计前向神经网络的方法。为解决神经网络权值随机初始化带来的问题,介绍了一种基于免疫的多样性模拟退火法(SAND算法)来进行神经网络权值初始化。仿真结果表明,该算法比混合遗传算法有更高的性能。     

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

In this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in offline mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation of different types of cooling process.     

利用遗传模拟退火算法优化神经网络结构

常用的神经网络是通过固定的网络结构得到最优权值,使网络的实用性受到影响。引入了一种基于方向的交叉算子和变异算子,同时把模拟退火算法引入了遗传算法,结合遗传算法和模拟退火算法的优点,提出了一种优化神经网络结构的遗传——模拟退火混合算法,实现了网络结构和权值的同时优化。仿真实验表明,与遗传算法和模拟退火算法相比,该算法优化的神经网络收敛速度较快、预测精度较高,提高了网络的处理能力。     

基于改进遗传模拟退火算法的BP神经网络的畸变校正研究

针对相机所采集的图像大多都存在畸变现象的问题,设计了基于改进遗传模拟退火算法的BP神经网络校正算法。该算法针对传统遗传算法易于收敛局部最优的问题,提出分段选择策略与随机抽样相结合的选择算子,自适应交叉与变异算子。在畸变校正中,该算法通过网络的输入输出建立理想点与畸变点的关系,使用改进的遗传模拟退火算法来优化神经网络中的阈值与权值,然后使用基于LM算法的BP神经网络进行局部优化,最后通过插值算法得到校正后的图像。实验表明,该算法能过较好的对图像进行畸变校正,同时与传统的BP神经网络算法相比精度更高,收敛速度更快。     

一种基于冷链低碳物流路径的混合优化算法

冷链物流运输路径优化问题是国内外学者近几年研究的一个热点问题.首先分析了冷链物流运输的特点和需求,并且提出了考虑综合代价、带硬时间窗和容量约束的单物流中心车辆路径优化模型,其中综合代价包括固定代价、车辆运输代价、货损代价、制冷代价、时间惩罚代价和碳排放代价.车辆路径规划问题是一个NP问题,需要使用智能优化算法对解空间进...     

一种基于思维进化算法的神经网络求解机器人逆运动学问题

研究了SCARA机器人的逆运动学问题,提出了一种采用思维进化算法来学习神经网络连接权值的方法。并将该算法成功地应用于求解机器人的逆运动学问题。计算机仿真表明,这种神经网络方法不仅具有较快的收敛速度,而且大大提高了求解的精度。     

Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach

In this paper, a fusion approach to determine inverse kinematics solutions of a six degree of freedom serial robot is proposed. The proposed approach makes use of radial basis function neural network for prediction of incremental joint angles which in turn are transformed into absolute joint angles with the assistance of forward kinematics relations. In this approach, forward kinematics relations of robot are used to obtain the data for training of neural network as well to estimate the deviation of predicted inverse kinematics solution from the desired solution. The effectiveness of the fusion process is shown by comparing the inverse kinematics solutions obtained for an end-effector of industrial robot moving along a specified path with the solutions obtained from conventional neural network approaches as well as iterative technique. The prominent features of the fusion process include the accurate prediction of inverse kinematics solutions with less computational time apart from the generation of training data for neural network with forward kinematics relations of the robot.     

A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators

In robotics, inverse kinematics problem solution is a fundamental problem in robotics. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the result obtained from the neural network requires to be improved for some sensitive tasks. In this paper, a neural-network committee machine (NNCM) was designed to solve the inverse kinematics of a 6-DOF redundant robotic manipulator to improve the precision of the solution. Ten neural networks (NN) were designed to obtain a committee machine to solve the inverse kinematics problem using separately prepared data set since a neural network can give better result than other ones. The data sets for the neural-network training were prepared using prepared simulation software including robot kinematics model. The solution of each neural network was evaluated using direct kinematics equation of the robot to select the best one. As a result, the committee machine implementation increased the performance of the learning.     

Medical image fusion based on hybrid intelligence

Medical image fusion combines complementary images from different modalities for proper diagnosis and surgical planning. A new approach for medical image fusion based on the hybrid intelligence system is proposed. This paper has integrated the swarm intelligence and neural network to achieve a better fused output. The edges are an important feature of an image and they are detected and optimized by using ant colony optimization. The detected edges are enhanced and it is given as the feeding input to the simplified pulse coupled neural network. The firing maps are generated and the maximum fusion rule is applied to get the fused image. The performance of the proposed method is compared both subjectively and objectively, with the genetic algorithm method, neuro-fuzzy method and also with the modified pulse coupled neural network. The results show that the proposed hybrid intelligent method performs better when compared to the existing computational and hybrid intelligent methods.     

网络结构拓扑扩展的混合遗传算法

计算机网络结构拓扑扩展时，经常要解决这样一类优化问题：在给定预算限制下，选择一组连接，使带来的利润最大。本文依据模拟退火技术和遗传算法，提出了一种混合遗传算法，能较快地得到最优解。