標(biāo)題: Titlebook: Neural Networks and Analog Computation; Beyond the Turing Li Hava T. Siegelmann Book 1999 Birkh?user Boston 1999 Natur.Theorie.complexity.c [打印本頁(yè)] 作者: commotion 時(shí)間: 2025-3-21 20:01
書目名稱Neural Networks and Analog Computation影響因子(影響力)
書目名稱Neural Networks and Analog Computation影響因子(影響力)學(xué)科排名
書目名稱Neural Networks and Analog Computation網(wǎng)絡(luò)公開度
書目名稱Neural Networks and Analog Computation網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Neural Networks and Analog Computation被引頻次
書目名稱Neural Networks and Analog Computation被引頻次學(xué)科排名
書目名稱Neural Networks and Analog Computation年度引用
書目名稱Neural Networks and Analog Computation年度引用學(xué)科排名
書目名稱Neural Networks and Analog Computation讀者反饋
書目名稱Neural Networks and Analog Computation讀者反饋學(xué)科排名
作者: INERT 時(shí)間: 2025-3-21 23:53 作者: 小口啜飲 時(shí)間: 2025-3-22 03:16
Universality of Sigmoidal Networks,dal-like” activation functions, suggesting that Turing universality is a common property of recurrent neural network models. In conclusion, the computational capabilities of sigmoidal networks are located in between Turing machines and advice Turing machines.作者: Bone-Scan 時(shí)間: 2025-3-22 06:48 作者: Allege 時(shí)間: 2025-3-22 09:09 作者: 詞根詞綴法 時(shí)間: 2025-3-22 16:21
Kolmogorov Weights: Between P and P/poly,recursive functions. This chapter proves the intuitive notion that as the real numbers used grow richer in information, more functions become computable. To formalize this statement, we need a measure by which to quantify the information contained in real numbers.作者: Exclude 時(shí)間: 2025-3-22 17:48
Stochastic Dynamics,ty in networks, e.g., [vN56, Pip90, Adl78, Pip88, Pip89, DO77a, DO77b], studied only acyclic architectures of binary gates, while we study general architectures of analog components. Due to these two qualitative differences, our results are totally different from the previous ones, and require new proof techniques.作者: 無(wú)關(guān)緊要 時(shí)間: 2025-3-22 23:32
Computational Complexity,computational models. Our presentation starts with elementary definitions of computational theory, but gradually builds to advanced topics; each computational term introduced is immediately related to neural models.作者: Epidural-Space 時(shí)間: 2025-3-23 04:27
Networks with Rational Weights, values only, here a neuron can take on countably infinite different values. The analysis of networks with rational weights is a prerequisite for the proofs of the real weight model in the next chapter. It also sheds light on the role of different types of weights in determining the computational capabilities of the model.作者: anagen 時(shí)間: 2025-3-23 07:26
Different-limits Networks,er is much wider than that of the previous chapter, and as a result the lower bound on its computational power is weaker. We prove that any function for which the left and right limits exist and are different can serve as an activation function for the neurons to yield a network that is at least as strong computationally as a finite automaton.作者: 范例 時(shí)間: 2025-3-23 11:32 作者: 裁決 時(shí)間: 2025-3-23 15:58
Networks with Rational Weights,et of rationals. In contrast to the case described in the previous chapter, where we dealt only with integer weights, and each neuron could assume two values only, here a neuron can take on countably infinite different values. The analysis of networks with rational weights is a prerequisite for the 作者: 溫和女人 時(shí)間: 2025-3-23 19:59
Networks with Real Weights,l practical purposes, useless since systems with infinitely precise constants cannot be built. However, the real weights are appealing for the . modeling of analog computation that occurs in nature, as discussed in Chapter 2. In nature, the fact that the constants are not known to us, or cannot even作者: 表主動(dòng) 時(shí)間: 2025-3-24 00:15 作者: certain 時(shí)間: 2025-3-24 06:16 作者: 啜泣 時(shí)間: 2025-3-24 09:21 作者: 爵士樂(lè) 時(shí)間: 2025-3-24 12:54 作者: 縫紉 時(shí)間: 2025-3-24 15:57 作者: Notorious 時(shí)間: 2025-3-24 20:06
The Model,In this chapter we introduce the formal model of the neural network to be utilized and analyzed in this book.作者: defibrillator 時(shí)間: 2025-3-25 02:09
Generalized Processor Networks,Up to this point we have analyzed in detail the computational properties of the analog recurrent neural network. From here on we turn to consider more general models of analog computation, and place our network within this wider framework.作者: 尖叫 時(shí)間: 2025-3-25 06:23 作者: 切碎 時(shí)間: 2025-3-25 08:00
Computation Beyond the Turing Limit,This chapter differs from the rest of the book. In addition to containing mathematical proofs, it also discusses possible philosophical consequences in the realm of super-Turing theories.作者: persistence 時(shí)間: 2025-3-25 15:33 作者: Bridle 時(shí)間: 2025-3-25 16:09 作者: 悲觀 時(shí)間: 2025-3-25 22:33 作者: Ancestor 時(shí)間: 2025-3-26 03:40 作者: enhance 時(shí)間: 2025-3-26 06:28
ibes the dynamical behavior of parallel updates. Some of the signals originate from outside the network and act as inputs to the system, while other signals are communicated back to the environment and are thus used to encode the end result of the computation.978-1-4612-6875-8978-1-4612-0707-8作者: 祖?zhèn)?nbsp; 時(shí)間: 2025-3-26 09:25
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