Intelligent Dendritic Neural Model for Classification Problems

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.

A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model

Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time.

Optimization of the PNG Law for a Dual-Spin Mortar with Fixed Canards

The limited instantaneous overload available and the curved trajectory lead to adaptivity problems for the proportional navigation guidance (PNG) of a guided mortar with a fixed-canard trajectory correction fuze. In this paper, the optimization of a PNG law with gravity compensation is established. Instead of using the traditional empirical method, the selection of the proportional navigation constants is formulated as an optimization problem, which is solved using an intelligent optimization algorithm. Two optimization schemes are proposed for constructing corresponding optimization models. In schemes 1 and 2, the sum squared error between the impact point and target and the circular error probability (CEP), respectively, are taken as the objective function. Monte Carlo simulations are conducted to verify the effectiveness of the two optimization schemes, and their guidance performance is compared through trajectory simulations. The simulation results show that the impact point dispersion can be efficiently reduced under both proposed schemes. Scheme 2 achieves a lower CEP, which is approximately 2.9 m and 2.4 times smaller than that achieved by scheme 1. Moreover, the mean impact point is closer to the target.

The Application of Cloud Computing Intelligent Optimization Algorithm in the Investigation of College Students’ English Autonomous Learning under the Multimedia Teaching Mode

Autonomous learning is what college students need to learn. For some students who have difficulties in self-study, this paper develops a cloud computing intelligent optimization algorithm in the multimedia teaching mode of college students’ English autonomous learning system. This platform can help self-learners learn translation, listening, speaking, and other skills. At the same time, the platform also includes a self-assessment system including diagnostic evaluation, formative evaluation, and summative evaluation. The final test shows that the recognition rate of the system can reach more than 95%, which can meet the needs of use.

Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence

This study aimed to explore the application of pelvic floor ultrasound under particle swarm intelligent optimization algorithm in the preoperative and postoperative evaluation of female stress urinary incontinence (SUI) and provide a theoretical basis for clinical diagnosis. In this study, 90 patients with SUI were enrolled, which were randomly and equally assigned into a blank group (healthy physical examination), control group (perineal ultrasound), and experimental group (pelvic floor ultrasound based on particle swarm intelligence optimization algorithm). The ultrasonic image segmentation and processing were carried out by a particle swarm intelligence optimization algorithm. Patients with stress incontinence were classified as group A, and patients without stress incontinence were classified as group B. The results of previous surgical examinations were the standard to judge the accuracy of pelvic floor ultrasound diagnosis based on the swarm intelligence optimization algorithm. The accuracy of diagnosing stress UI in the experimental group was 90.38%, which was significantly higher than that of the control group (54.31%) and the blank group (38.95%) ( P < 0.05). The formation percentage of the urethral internal orifice in the experimental group was 82.5%, which was significantly higher than that of the control group (65.4%) and the blank group (12.5%), and there was a statistical difference among the groups ( P < 0.05). In the resting state, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was greater than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was smaller than in the blank group, and there were statistical differences among the groups ( P < 0.05). In the state of Valsalva, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was smaller than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was greater than that in group B. The distance of the bladder neck shifting downward was greater than that in group B, and there were statistical differences among the groups ( P < 0.05). In short, the pelvic floor ultrasound based on the particle swarm intelligent optimization algorithm was accurate in the diagnosis of stress UI. The application of pelvic floor ultrasound in the diagnosis of UI provided image data objectively for clinical diagnosis and had a high application value.