scholarly journals TheI-VCharacteristic Prediction of BCD LV pMOSFET Devices Based on an ANFIS-Based Methodology

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Shen-Li Chen
Keyword(s):  
Fuzzy Theory ◽  
Systematic Evaluation ◽  
Mos Transistors ◽  
Inference System ◽  
Submicron Devices ◽  
Precise Prediction ◽  
A New Technique ◽  
Adaptive Neurofuzzy Inference System ◽  
Error Percentage ◽  
Good Agreement

Comprehensive and predictive modeling of submicron devices using the traditional TCAD EDA tools of device simulation has become increasingly perplexing due to a lack of reliable models and difficulties in calibrating available device models. This paper proposes a new technique to model BCD submicron pMOSFET devices and to predict device behaviors under different bias conditions and different geometry dimensions by using the adaptive neurofuzzy inference system (ANFIS), which combines fuzzy theory and adaptive neuronetworking. Here, the power of using ANFIS to realize theI-Vbehaviors is demonstrated in these p-channel MOS transistors. After a systematic evaluation, it can be found that the predicting results ofI-Vbehaviors of complicated submicron pMOSFETs by ANFIS are compared with the actual diagnostic experiment data, and a good agreement has been obtained. Furthermore, the error percentage was no greater than 2.5%. As such, the demonstrated benefits of this new proposed technique include precise prediction and easier implementation.

Artificial-Neural-Network Prediction of Device Behaviors in Submicron MOS Transistors

2013 ◽  
Vol 634-638 ◽  
pp. 2442-2445
Author(s):  
Shen Li Chen ◽  
Ying Der Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Theory ◽  
Mos Transistors ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Artificial Neural ◽  
A New Technique ◽  
Artificial Neural Network Ann ◽  
Good Agreement

A new technique is presented for modeling submicron MOSFET devices and predicting the MOSFET device behaviors by using fuzzy theory and artificial neural network (ANN). The power of ANNs used as a realization of I-V characterizations is demonstrated on the submicron MOS transistors. The prediction results are compared with experimental data of the actual devices and obtained a good agreement under different bias situations.

scholarly journals Approach to the Highest HXR Yield in Plasma Focus Device Using Adaptive Neurofuzzy Inference System to Optimize Anode Configuration

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
M. Mahtab ◽  
M. Taghipour ◽  
G. H. Roshani ◽  
M. Habibi
Keyword(s):  
Relative Error ◽  
Plasma Focus ◽  
Low Cost ◽  
Discharge Voltage ◽  
Inference System ◽  
X Ray ◽  
Mean Relative Error ◽  
Testing Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Good Agreement

Adaptive neurofuzzy inference system (ANFIS) is investigated to optimize the configuration of anode shape in plasma focus devices to achieve the highest X-ray yield. Variables of discharge voltage, filling gas pressure, and angles of anode slopes (Φ1 and Φ2) are chosen as input parameters, while the output is designated to be the radiated hard X-ray intensity. The trained ANFIS has achieved good agreement with the experimental results and has mean relative error percentages (MRE%) 1.12% and 2.18% for training and testing data, respectively. The study demonstrates that adaptive neurofuzzy inference system is useful, reliable, and low-cost way to interpret the highest X-ray yield and corresponding anode configuration in plasma focus devices.

Antiferromagnetic exchange in triclinic crystals of tetra-u-benzoato- bits (4-methylquinoline) dicobalt II

1978 ◽  
Vol 360 (1701) ◽  
pp. 191-210 ◽  
Keyword(s):  
Cobalt Complex ◽  
Preceding Paper ◽  
Transverse Magnetic ◽  
Antiferromagnetic Coupling ◽  
Temperature Range ◽  
General Terms ◽  
Carboxylate Groups ◽  
A New Technique ◽  
Unambiguous Conclusion ◽  
Good Agreement

The magnetic susceptibilities of tetra- u -benzoato- bis (4-methylquinoline) dicobalt ii have been measured and interpreted within the theoretical model described in the preceding paper. Crystals of the title complex are triclinic, a circumstance which has lead to the development of a new technique for the measurement of triclinic crystal susceptibilities using a Faraday balance. The technique is discussed in general terms and is applicable with Faraday equipment employing either longitudinal or, as here, transverse magnetic fields. The magnetic tensor for this binuclear cobalt complex has been determined throughout the temperature range 20- 300 K. Good agreement between these results and those calculated from the quantum mechanical model have been obtained in the temperature range 90-300 K. At lower temperatures, a probable small paramagnetic impurity prevents useful theoretical treatm ent. There emerges an unambiguous conclusion that the antiferromagnetic coupling between the cobalt atoms is almost completely determined by interaction between metal xy orbitals, presumably via a superexchange process involving the delocalized n bonding framework of the bridging carboxylate groups.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

Excited State Absorption in Pr3+ Doped Fluorozirconate Glass

1991 ◽  
Vol 244 ◽  
Author(s):  
R. S. Quimby ◽  
B. Zheng
Keyword(s):  
Excited State ◽  
Excited State Absorption ◽  
Fiber Amplifier ◽  
Pump Probe ◽  
Probe Technique ◽  
Zblan Glass ◽  
Fluorozirconate Glass ◽  
A New Technique ◽  
Doped Glass ◽  
Good Agreement

ABSTRACTThe excited state absorption (ESA) spectrum for Pr3+ doped ZBLAN glass is determined using a new technique based on the McCumber theory [D.E. McCumber, Phys. Rev. 136, A954 (1964)]. ESA peaks at 1380 and 840 nm are found, corresponding to transitions from the 1G4 to the 1D2 and 1I6 levels, respectively. ESA at the fiber amplifier pump wavelength 1.017 μm is found to be very small. The new method is also applied to Er+ doped glass, and good agreement is obtained between the resulting ESA spectrum and previous measurements using a traditional pump-probe technique.

scholarly journals A novel rapid method of purely elastic solution correction to estimate multiaxial elastic-plastic behaviour

2019 ◽  
Vol 6 (3) ◽  
pp. 269-283
Author(s):  
Nicolas Antoni
Keyword(s):  
Three Dimensional ◽  
Elastic Solution ◽  
Upper And Lower Bounds ◽  
Efficient Design ◽  
Effective Parameters ◽  
Relative Deviation ◽  
Elastic Plastic ◽  
Plastic Behaviour ◽  
A New Technique ◽  
Good Agreement

Abstract In structural analysis, it is of paramount importance to assess the level of plasticity a structure may experience under monotonic or cyclic loading as this may have a significant impact, particularly in fatigue analysis for singular areas. For efficient design analyses, it is often searched for a compromise in accuracy that consists in correcting a purely elastic analysis, generally simpler and faster to obtain compared to a full non-linear Finite Element (FE) analysis involving elastic-plastic behaviour, to estimate the actual elastic-plastic solution. There exists a great number of correction techniques in the literature among which the most famous and commonly used are Neuber and ESED energy-based methods. Nonetheless, both of them are known to provide respectively upper and lower bounds of the exact solution in most cases, with a relative deviation depending on the level of multiaxiality and on the amount of stress redistribution due to yielding. The new methodology presented in this paper is based on the well-known multiaxial Radial Return Method (RRM) revisited using effective parameters approach. By essence, it is fast and can be applied either to analytical elastic problems or to more complex three-dimensional elastic FE analyses. The accuracy of the proposed method is assessed by direct comparison with results from Neuber and ESED methods on various examples. It is also shown for each of them that this new method is very good agreement with the exact elastic-plastic solution. Highlights A new technique of purely elastic solution correction is presented and evaluated. The proposed method relies on the modification of Return Radial Method (RRM) considering effective parameters in lieu of initial elastic tensor. The obtained equation preserves the simplicity and efficiency of other well-known energy-based methods such as Neuber and ESED. It is shown on several examples that the proposed technique is in very good agreement with the exact or FE elastic-plastic solution, with very low relative deviation.

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