Input Selection Applications

2020 ◽  
pp. 1095-1123
Author(s):  
Oliver Nelles
Keyword(s):  
Input Selection

scholarly journals FPGA Implementation of Reconfigurable Finite State Machine with Input Multiplexing Architecture Using Hungarian Method

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Nitish Das ◽  
P. Aruna Priya
Keyword(s):  
Finite State Machine ◽  
Graph Matching ◽  
Digital Signal ◽  
State Machine ◽  
Fpga Implementation ◽  
Greedy Heuristic ◽  
Matching Problem ◽  
Input Selection ◽  
Weighted Bipartite Graph ◽  
Finite State

The mathematical model for designing a complex digital system is a finite state machine (FSM). Applications such as digital signal processing (DSP) and built-in self-test (BIST) require specific operations to be performed only in the particular instances. Hence, the optimal synthesis of such systems requires a reconfigurable FSM. The objective of this paper is to create a framework for a reconfigurable FSM with input multiplexing and state-based input selection (Reconfigurable FSMIM-S) architecture. The Reconfigurable FSMIM-S architecture is constructed by combining the conventional FSMIM-S architecture and an optimized multiplexer bank (which defines the mode of operation). For this, the descriptions of a set of FSMs are taken for a particular application. The problem of obtaining the required optimized multiplexer bank is transformed into a weighted bipartite graph matching problem where the objective is to iteratively match the description of FSMs in the set with minimal cost. As a solution, an iterative greedy heuristic based Hungarian algorithm is proposed. The experimental results from MCNC FSM benchmarks demonstrate a significant speed improvement by 30.43% as compared with variation-based reconfigurable multiplexer bank (VRMUX) and by 9.14% in comparison with combination-based reconfigurable multiplexer bank (CRMUX) during field programmable gate array (FPGA) implementation.

NOVEL VOLTAGE-MODE UNIVERSAL FILTER USING ONLY TWO CDBAs

2005 ◽  
Vol 14 (01) ◽  
pp. 159-164 ◽  
Author(s):  
SUDHANSHU MAHESHWARI ◽  
IQBAL A. KHAN
Keyword(s):  
Pass Band ◽  
Universal Filter ◽  
Input Selection ◽  
Voltage Mode ◽  
Single Output ◽  
Control Computer ◽  
Low Pass ◽  
Band Pass ◽  
Pass Through ◽  
High Pass

A novel voltage-mode universal filter employing only two current differencing buffered amplifiers (CDBAs) is proposed. The filter uses four inputs and single output to realize six responses, viz. low-pass, high-pass, inverting band-pass, noninverting band-pass, band-elimination, and all-pass through input selection with independent pole-Q control. Computer simulation results using SPICE are also given to verify the theory.

Neural model input selection for a MIMO chemical process

2000 ◽  
Vol 13 (1) ◽  
pp. 15-23 ◽  
Author(s):  
D.L. Yu ◽  
J.B. Gomm ◽  
D. Williams
Keyword(s):  
Chemical Process ◽  
Neural Model ◽  
Input Selection ◽  
Model Input ◽  
Selection For

scholarly journals Submodular Input Selection for Synchronization in Kuramoto Networks

2020 ◽  
Author(s):  
Dinuka Sahabandu ◽  
Andrew Clark ◽  
Linda Bushnell ◽  
Radha Poovendran
Keyword(s):  
Input Selection ◽  
Selection For

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

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