Conventional system models such as the finite impulse response (FIR) model, autoregressive external input (ARX) model, time delay neural network (TDNN), and recurrent neural network (RNN) depend on short-term memory when modeling a discrete time system. However, short-term memory can be inefficient with a varying appearance speed of I/O data. This inefficiency is referred to herein as the Varying Appearance Speed Problem (VASP) and demonstrated by analyzing impulse and frequency responses. Simulation results indicate that the varying appearance speed leads to asymmetrical cycles. Unable to prevent the memory effect from extensively disturbing the next output cycle, conventional models simulate the systems inaccurately. A solution using rate independent memory is then proposed. Only concerned with the previous extreme inputs, rate independent memory differs from short-term memory and potentially prevents a system model from the impact of varying appearance speeds. To demonstrate the VASP and verify the proposed model, this study conducts three experiments, i.e. (a) learning random step trajectories of circular and trefoil shapes, (b) modeling the relationship between the economic leading and coincident indexes, (c) simulating the connection between the ground-water level and land subsidence. In contrast to conventional models, the model presented here performs better in terms of mean square errors.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
Jyh-Da WEI, Chuen-Tsai SUN, "Varying Appearance Speed Problem in System Modeling and a Solution via Rate Independent Memory" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 5, pp. 1119-1128, May 2002, doi: .
Abstract: Conventional system models such as the finite impulse response (FIR) model, autoregressive external input (ARX) model, time delay neural network (TDNN), and recurrent neural network (RNN) depend on short-term memory when modeling a discrete time system. However, short-term memory can be inefficient with a varying appearance speed of I/O data. This inefficiency is referred to herein as the Varying Appearance Speed Problem (VASP) and demonstrated by analyzing impulse and frequency responses. Simulation results indicate that the varying appearance speed leads to asymmetrical cycles. Unable to prevent the memory effect from extensively disturbing the next output cycle, conventional models simulate the systems inaccurately. A solution using rate independent memory is then proposed. Only concerned with the previous extreme inputs, rate independent memory differs from short-term memory and potentially prevents a system model from the impact of varying appearance speeds. To demonstrate the VASP and verify the proposed model, this study conducts three experiments, i.e. (a) learning random step trajectories of circular and trefoil shapes, (b) modeling the relationship between the economic leading and coincident indexes, (c) simulating the connection between the ground-water level and land subsidence. In contrast to conventional models, the model presented here performs better in terms of mean square errors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_5_1119/_p
Copy
@ARTICLE{e85-a_5_1119,
author={Jyh-Da WEI, Chuen-Tsai SUN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Varying Appearance Speed Problem in System Modeling and a Solution via Rate Independent Memory},
year={2002},
volume={E85-A},
number={5},
pages={1119-1128},
abstract={Conventional system models such as the finite impulse response (FIR) model, autoregressive external input (ARX) model, time delay neural network (TDNN), and recurrent neural network (RNN) depend on short-term memory when modeling a discrete time system. However, short-term memory can be inefficient with a varying appearance speed of I/O data. This inefficiency is referred to herein as the Varying Appearance Speed Problem (VASP) and demonstrated by analyzing impulse and frequency responses. Simulation results indicate that the varying appearance speed leads to asymmetrical cycles. Unable to prevent the memory effect from extensively disturbing the next output cycle, conventional models simulate the systems inaccurately. A solution using rate independent memory is then proposed. Only concerned with the previous extreme inputs, rate independent memory differs from short-term memory and potentially prevents a system model from the impact of varying appearance speeds. To demonstrate the VASP and verify the proposed model, this study conducts three experiments, i.e. (a) learning random step trajectories of circular and trefoil shapes, (b) modeling the relationship between the economic leading and coincident indexes, (c) simulating the connection between the ground-water level and land subsidence. In contrast to conventional models, the model presented here performs better in terms of mean square errors.},
keywords={},
doi={},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - Varying Appearance Speed Problem in System Modeling and a Solution via Rate Independent Memory
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1119
EP - 1128
AU - Jyh-Da WEI
AU - Chuen-Tsai SUN
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2002
AB - Conventional system models such as the finite impulse response (FIR) model, autoregressive external input (ARX) model, time delay neural network (TDNN), and recurrent neural network (RNN) depend on short-term memory when modeling a discrete time system. However, short-term memory can be inefficient with a varying appearance speed of I/O data. This inefficiency is referred to herein as the Varying Appearance Speed Problem (VASP) and demonstrated by analyzing impulse and frequency responses. Simulation results indicate that the varying appearance speed leads to asymmetrical cycles. Unable to prevent the memory effect from extensively disturbing the next output cycle, conventional models simulate the systems inaccurately. A solution using rate independent memory is then proposed. Only concerned with the previous extreme inputs, rate independent memory differs from short-term memory and potentially prevents a system model from the impact of varying appearance speeds. To demonstrate the VASP and verify the proposed model, this study conducts three experiments, i.e. (a) learning random step trajectories of circular and trefoil shapes, (b) modeling the relationship between the economic leading and coincident indexes, (c) simulating the connection between the ground-water level and land subsidence. In contrast to conventional models, the model presented here performs better in terms of mean square errors.
ER -