Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Abstract

The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Application of Improved BQGA in Robot Kinematics Inverse Solution

“…However, in practice it is the opposite; that is, the end position and posture are known to solve the value of the angle of each joint (inverse solution). In order to evaluate each set of inverse solutions, we have established (16) [4,29] which transforms the inverse solution problem into a multivariate function to find the extremum problem.…”

“…where A is the actual position and posture matrix of the robot arm established by the D-H parameter method, and T is the set target position and posture matrix, ‖Α −T‖ 2 is the two norm of A and T [30] which represents the distance between the two matrices. Available from (16), when the actual position and posture of the robot arm are equal to or infinitely close to the target position and posture, the minimum value of the evaluation function is zero or approximately zero.…”

“…Quantum Genetic Algorithm (QGA) is popular among scholars because of its convenience and efficiency. Therefore, many improved algorithms such as real-coded QGA [16], adaptive double-chain QGA [17], and Bloch Quantum Genetic Algorithm (BQGA) [18] have emerged.…”

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

“…Motivated by previous studies which show that the consumption of primary energy could be approximated by using linear or nonlinear econometrics with economic and/or noneconomic indicators, mathematical models [20] and especially the ARIMA model [21] and the BP neural network [22] are common models used for prediction. erefore, in this paper, we will adopt the ARIMA model and the BP neural network one to make predictions and draw conclusions through making comparisons of models' correctness.…”

Adaptive Collaborative Quantum-Inspired Evolutionary Algorithm for Global Numerical Functions

“…Li Panchi et al used Bloch coordinates comprise gene chain to represent quantum individuals, which can accelerate the convergence process [3]; Zhao Shuanfeng et al proposed a real-coded chaotic quantum genetic algorithm based on the chaotic and coherent characters of Q-bits [4]; Zhang Lei et al adopt phase angle and its corresponding real mapping to construct quantum individual with probabilistic representation, which can represent all the feasible solution probabilistically [5]; Jianyong Liu et al had thought conventional quantum genetic algorithm (QGA) could be used to solve combinatorial optimization problems well than real-valued problems, so they proposed Real-coded QGA [6].…”