前饋神經網路

前饋神經網絡（英文：Feedforward Neural Network），是指神經網路的識別-推理架構。人工神經網路架構是以輸入乘上權重來獲得輸出（輸入對輸出）：前饋。^[1]循環神經網絡或有迴圈的神經網路允許後處理階段的資訊回饋到前處理階段進行序列處理。^[2]然而，在推論的每個階段，前饋乘法仍然是核心，對於反向傳播或透過時間的反向傳播來說是不可或缺的。^[3]^[4]^[5]^[6]^[7]因此，神經網路不能包含負反饋或正反饋等回饋，在負反饋或正反饋中，輸出會回饋到相同的輸入並對其進行修改，因為這會形成一個無限循環，不可能通過反向傳播在時間上倒退以產生錯誤信號。這個問題和命名似乎是一些電腦科學家和其他研究腦部網路領域的科學家之間的混淆點。^[8]

前饋神經網絡為人工智能領域中，最早發明的簡單人工神經網絡類型。在它內部，參數從輸入層向輸出層單向傳播。有異於循環神經網絡，它的內部不會構成有向環。^[9]

單層感知機

多層感知機

參考

^ Zell, Andreas. Simulation Neuronaler Netze [Simulation of Neural Networks] 1st. Addison-Wesley. 1994: 73. ISBN 3-89319-554-8 （German）.
^ Schmidhuber, Jürgen. Deep learning in neural networks: An overview. Neural Networks. 2015-01-01, 61: 85–117. ISSN 0893-6080. PMID 25462637. S2CID 11715509. arXiv:1404.7828  . doi:10.1016/j.neunet.2014.09.003 （英語）.
^ Linnainmaa, Seppo. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors (學位論文). University of Helsinki: 6–7. 1970 （芬蘭語）.
^ Kelley, Henry J. Gradient theory of optimal flight paths. ARS Journal. 1960, 30 (10): 947–954. doi:10.2514/8.5282.
^ Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
^ Werbos, Paul. Applications of advances in nonlinear sensitivity analysis (PDF). System modeling and optimization. Springer. 1982: 762–770 [2 July 2017]. （原始內容存檔 (PDF)於14 April 2016）.
^ Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. "Learning Internal Representations by Error Propagation". David E. Rumelhart, James L. McClelland, and the PDP research group. (editors), Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press, 1986.
^ Achler, T. What AI, Neuroscience, and Cognitive Science Can Learn from Each Other: An Embedded Perspective. Cognitive Computation. 2023 （英語）.
^ 戴葵. 譯.（機械工業出版社）《神經網絡設計》

[Zell1994p73-1] Zell, Andreas. Simulation Neuronaler Netze [Simulation of Neural Networks] 1st. Addison-Wesley. 1994: 73. ISBN 3-89319-554-8 （German）.

[2] Schmidhuber, Jürgen. Deep learning in neural networks: An overview. Neural Networks. 2015-01-01, 61: 85–117. ISSN 0893-6080. PMID 25462637. S2CID 11715509. arXiv:1404.7828  . doi:10.1016/j.neunet.2014.09.003 （英語）.

[lin1970-3] Linnainmaa, Seppo. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors (學位論文). University of Helsinki: 6–7. 1970 （芬蘭語）.

[kelley1960-4] Kelley, Henry J. Gradient theory of optimal flight paths. ARS Journal. 1960, 30 (10): 947–954. doi:10.2514/8.5282.

[5] Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961

[werbos1982-6] Werbos, Paul. Applications of advances in nonlinear sensitivity analysis (PDF). System modeling and optimization. Springer. 1982: 762–770 [2 July 2017]. （原始內容存檔 (PDF)於14 April 2016）.

[rumelhart1986-7] Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. "Learning Internal Representations by Error Propagation". David E. Rumelhart, James L. McClelland, and the PDP research group. (editors), Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press, 1986.

[AINeuroCog-8] Achler, T. What AI, Neuroscience, and Cognitive Science Can Learn from Each Other: An Embedded Perspective. Cognitive Computation. 2023 （英語）.

[9] 戴葵. 譯.（機械工業出版社）《神經網絡設計》

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