A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks

Ma, Weihong; Li, Qifeng; Li, Jiawei; Ding, Luyu; Yu, Qinyang

doi:10.1016/j.inpa.2020.07.001

Cited by 14 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the ninth step, randomly select a new input sample as the BP neural network input and return to the third step until all input samples are trained. The tenth step is to randomly re-select an input sample from the m input samples and return to the third step until the global error function E of the entire network is less than the preset precision [23]- [26].…”

Section: B Bp Neural Network Learning Stepsmentioning

confidence: 99%

Construction of an Art Education Teaching System Assisted by Artificial Intelligence

Wang,

Sun

2024

IJACSA

View full text Add to dashboard Cite

With the continuous progress of art education and artificial intelligence technology, traditional music teaching models are facing transformation. This article aims to construct an art education and teaching system based on artificial intelligence, especially for teaching music sound recognition. Through in-depth research, we have designed a music sound recognition system that uses Mel frequency cepstral coefficient (MFCC) for feature parameter extraction, and combines BP neural network algorithm to construct a music sound learning model. The main purpose is to improve the efficiency and accuracy of music teaching through artificial intelligence technology. The main challenge we face in this process is how to effectively extract the features of music sounds and accurately identify different tones through algorithms. By using the MFCC algorithm, we have successfully solved this problem as it can effectively describe the time-frequency characteristics of music sound. Our proposed music sound learning model is based on a BP neural network, which trains the network to learn the mapping relationship between music sound and pitch. The experiment used piano sound as an example to verify the accuracy and reliability of the system. The simulation experiments conducted in MATLAB environment show that our system can accurately recognize and extract the main frequency of music, and has higher performance compared to traditional methods.

show abstract

Section: B Bp Neural Network Learning Stepsmentioning

confidence: 99%

Construction of an Art Education Teaching System Assisted by Artificial Intelligence

Wang,

Sun

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…However, due to the fact that the learning process of ELMAN neural networks is based on gradient descent, this may result in the network only being able to find local optimal solutions and unable to find global optimal solutions. BP neural networks [37,38] are suitable for solving problems that traditional algorithms or computational methods find difficult to solve. A BP neural network is a mature nonlinear mapping method for solving real-world problems.…”

Section: Data Processingmentioning

confidence: 99%

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

Liu,

Zhang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO2) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO2 is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO2 concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.

show abstract

“…l is the number of hidden layers, Where m is the number of neurons in the input layer, n is the number of neurons in the output layer, and a is a constant between (Murtagh and Pierre, 2014 ; Ma et al, 2020 ). According to a large number of experimental data, this algorithm sets a = 3 .…”

Section: Optimization Of Kmeans Clustering Algorithmmentioning

confidence: 99%

“…At present, data labels are usually difficult to obtain, the manifestation of heterogeneous data itself is extremely different. In addition, the noise and outliers contained in the original data put forward higher requirements on the robustness of the algorithm (Ma et al, 2020 ; Yang et al, 2020 ). In particular, there are often more noise and outliers in multi-source heterogeneous data, which greatly affects the performance of the algorithm in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

et al. 2021

View full text Add to dashboard Cite

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

show abstract

A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks

Cited by 14 publications

References 46 publications

Construction of an Art Education Teaching System Assisted by Artificial Intelligence

Construction of an Art Education Teaching System Assisted by Artificial Intelligence

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

Contact Info

Product

Resources

About