Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

Relaxed Skolam Mean Labeling of 5 – Star Graphs with Partition 3, 2

In this article, our main topic is about the existence of relaxed skolem mean labeling for a 5 – star graph G = K1,α ∪ K1,α ∪ K1,α ∪ K1, β ∪ K1, β 1 2 3 1 2 with partition 3, 2 with a certain condition. By using the trial and error method we find the existence of the relaxed skolam mean labeling of 5 - star graph with partition 3, 2 with a specific condition.

Relaxed Skolam Mean Labeling of 6 – Star Graphs with Partition 3, 3

Existence Relaxed skolam mean labeling for a 6 – star graph 3 G = K1,α K1,α K1,α K1,β K1,β K1,β 1 2 3 1 2 ∪ ∪ ∪ ∪ ∪ with partition 3,3 with a certain condition is the core topic of the following article. Trial and error method is used to find the existence of the relaxed skolam mean labeling of 6 - star graph with partition 3, 3 holding a specific condition.

A compliance modeling method of flexible rotary joint for collaborative robot using passive network synthesis theory

Collaborative robots have become a research focus because of their wide applications. However, the previous compliance design method of the flexible rotary joint for collaborative robot mainly relied on experience of designers, and “trial and error” method is usually adopted, no feasible and systematic theory for the designer to select numerical value and series-parallel connection mode of the springs and dampers for the flexible rotary joint. Thus, developing a feasible compliance modeling theory to guide the design of the flexible rotary joint is a particularly challenging task. The main contribution of this paper is to present a novel and effective compliance modeling theory of the flexible rotary joint for collaborative robot based on electrical and mechanical passive network synthesis, to provide theoretical and systematic guidances for compliance design of the flexible rotary joint. First, inerter element is introduced into the mechanical system, and the compliance of the flexible rotary joint is expressed as an angular velocity admittance function using electrical and mechanical network analogy. Then, by passive network synthesis theory, the three kinds of compliance realization forms of rational function and four-element compliance realization conditions of biquadratic function for the flexible rotary joint are given using inerters, springs, and dampers. Moreover, numerical examples and simulations are conducted to illustrate effectiveness of the proposed compliance realization method. Finally, discussions are given to illustrate advantages of the proposed compliance modeling and design methods compared with the previous method.

Directivity Enhancement in Non-Reciprocal Bandpass Filters using an Evolutionary Algorithm

In this letter, a non-reciprocal filter with enhanced directivity is analyzed methodically and the filter parameters are optimized using an evolutionary algorithm. The return loss, insertion loss, and isolation characteristics of the filter exhibit a trade-off that makes manual tuning a trial-and-error method. The veracity of the numerical modeling is conformed by designing a 150 MHz lumped element non-reciprocal bandpass filter based on the parameters extracted using an evolutionary algorithm based particle swarm optimization (PSO). The simulated and measured results comply well with the modeling and the results exhibit maximum directivity of 28.2 dB without degradation in insertion loss (1.1 dB) and return loss (16.2 dB) within the passband. The algorithm can be utilized in designing non-reciprocal filters having different center frequencies and bandwidths.

Directivity Enhancement in Non-Reciprocal Bandpass Filters using an Evolutionary Algorithm

In this letter, a non-reciprocal filter with enhanced directivity is analyzed methodically and the filter parameters are optimized using an evolutionary algorithm. The return loss, insertion loss, and isolation characteristics of the filter exhibit a trade-off that makes manual tuning a trial-and-error method. The veracity of the numerical modeling is conformed by designing a 150 MHz lumped element non-reciprocal bandpass filter based on the parameters extracted using an evolutionary algorithm based particle swarm optimization (PSO). The simulated and measured results comply well with the modeling and the results exhibit maximum directivity of 28.2 dB without degradation in insertion loss (1.1 dB) and return loss (16.2 dB) within the passband. The algorithm can be utilized in designing non-reciprocal filters having different center frequencies and bandwidths.

Crop yield forecasting of paddy and sugarcane through modified Hendrick and Scholl technique for south Gujarat

Regression models by modified Hendrick and Scholl technique were developed on paddy and sugarcane for six districts of south Gujarat. The data on the yield and weather parameters were analyzed for 27 years. The 25 year data was used for development of the model. The validation of model was done using data set of 2010 and 2011. The stepwise regression analysis was executed by trial and error method to obtain the finest combination of predictors, significant at 5% level. The multiple regression techniques was used for fitting of the model and decided best by highest R2 and lowest percent error. All crop yield forecasting models gave good estimates and produced error percent within acceptable range. Analysis revealed that the model error percent of paddy and sugarcane for respective crop growing districts were -10.0 to 8.1% and -12.2 to 1.5% respectively. Crop yield forecasting for year 2012 based on validated model was made for the districts of Navsari, Surat, Bharuch, Valsad, Narmada and Tapi.

A mathematical model of a brushed DC motor system

Mathematical model has been proposed for some system that involves a brushed DC motor and it is widely used in industry. Brushed DC motor ideals for applications with a low- torque, manage to change pace or speed and it is widely used in many applications such as x-y table positioning system, conveyor systems and other system that required to use the features that brushed DC motor have. Mathematical model of brushed DC motor in order to verify the performance of the DC motor. In this paper, mathematical model of brushed DC motor will be derived from a brushed DC motor circuit that consist of two parts that are electrical and mechanical part. To validate the functionality of mathematical model, the performance of the brushed DC motor without any controller will be compared with the brushed DC motor with the presence of PI-PD controller that will be tuned by trial-and-error method. Performances of both brushed DC motor with and without controller will be compared in terms of transient response which are, rise time, Tr, settling time, Ts, steady state error, ess and lastly percentage overshoot. At the end of the study, the brushed DC motor with PI-PD controller show a better performance compared to the brushed DC motor without any controller.