Application Of Backpropagation Algorithm Research Articles

Brain Liberation - An Anthropological Reflection on the Threat of Artificial Intelligence

Abstract This study examines the threat of AI to the emancipation of the human brain, with a particular focus on its impact on human logical thinking and creativity. It reveals the centrality of AI in knowledge engineering by analyzing expert systems constructed by the Symbolist school, which emphasize the role of knowledge bases and reasoning machines by simulating human logical thinking. The study also examines how AI affects modern human subjectivity, utilizing the “Valley of Terror” curve theory to explain the changes in human attitudes toward AI Further, the study introduces human brain-based AI models, such as the application of back-propagation algorithms in neural networks, as well as a cognitive-based model of knowledge operations in the human brain. The results show that the development of AI poses a threat to human brain power, especially in information processing and decision making. AI can replace human brain functions, leading to human logic and creativity degradation. For example, the application of ChatGPT in education can significantly improve students’ test rankings and reduce the reliance on individual thinking skills. In summary, AI may lead to the degradation of human logical thinking and creative ability while improving efficiency and convenience, affecting the overall development of human beings. Therefore, the focus should be on balancing the relationship between the utilization of AI and the development of human brain capacity, significantly to enhance the cultivation of human creativity and imagination in education and innovation.

Research on Forecast Model Based on BP Neural Network Algorithm

Back propagation Neural Network is a well-known method of prediction. Due to the development of the neural network technology, there has been an increasing amount of neural network application research in the last decade, with a majority of application focusing on financial decisions. Firstly, this paper demonstrates the algorithm of back propagation Neural Network. Then, the application of Back propagation algorithm in prediction sector is explained in detail through the design of pre-warning model of financial distress for real estate industry in China. Meanwhile Z-score model is utilized to produce the only output nodes of Back propagation Neural Network. The research can provide evidence of forecast performance based on BP Neural network technique.

Application of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining technique

ABSTRACTThe paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA – training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network.

Exploring optimal architecture of Multi-layered Feed- forward (MLFNN) as Bidirectional Associative Memory (BAM) for Function Approximation

Function approximation is an instance of supervised learning which is one of the most studied topics in machine learning, artificial neural networks, pattern recognition, and statistical curve fitting. In principle, any of the methods studied in these fields can be used in reinforcement learning. Multi-layered feed-forward neural networks (MLFNN) have been extensively used for the purpose of function approximation. Another class of neural networks, BAM, has also been studied and experimented for pattern mapping problems and many variations have been reported in literature. In the present study the application of back propagation algorithm to MLFNN has been proposed in such a way that feed-forward architecture behaves like BAM. Various architectures consisting of fourlayers have been explored in quest of finding the optimal architecture for the example function.

Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidirectional Associative Memory (BAM) for Function Approximation

Function approximation is to find the underlying relationship from a given finite input-output data. It has numerous applications such as prediction, pattern recognition, data mining and classification etc. Multi- layered feed-forward neural networks (MLFNNs) with the use of back propagation algorithm have been extensively used for the purpose of function approximation recently. Another class of neural networks BAM has also been experimented for the same problem with lot of variations. In the present paper we have proposed the application of back propagation algorithm to MLFNN in such a way that it works like BAM and the result thus presented show greater and speedy approximation for the example function.