Time-Delay Neural Networks : Representation and Induction of Finite-State Machines  

Oleh:

Horne, B. G. ; Clouse, D. S. ; Giles, C. L. ; Cottrell, G. W.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 5 (1997), page 1065-1070.
Topik: MACHINE; time - delay; neural networks; representation; induction; machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

iN this work, we characterize and contrast the capabilities of the general class of time - delay neural networks (TDNNs) with input delay neural networks (IDNNs), the subclass of TDNN s with delays limited to the inputs. Each class of networks is capable of representing the same set of languages, those embodied by the definite memory machines (DMM s), a subclass of finite - state machines. We demonstrate the close affinity between TDNN s and DMM languages by learning a very large DMM (2048 states) using only a few training examples. Even though both architectures are capable of representing the same class of languages, they have distinguishable learning biases. Intuition suggests that general TDNN s which include delays in hidden layers should perform well, compared to IDNN s, on problems in which the output can be expressed as a function on narrow input windows which repeat in time. On the other hand, these general TDNN s should perform poorly when the input windows are wide, or there is little repetition. We confirm these hypotheses via a set of simulations and statistical analysis.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali