The hybrid nonlinear dynamics of the resonator comprise a transient nonlinear response, and a Duffing nonlinear response is first used for reservoir computing. Here we propose a novel nondelay-based reservoir computer using only a single micromechanical resonator with hybrid nonlinear dynamics that removes the usually required delayed feedback loop. It has been successfully implemented by injecting an input signal into a spatially extended reservoir of nonlinear nodes or a temporally extended reservoir of a delayed feedback system to perform temporal information processing. 955, National Instruments, NI LabVIEW 2014 Real-Time Error Dialog.Reservoir computing is a potential neuromorphic paradigm for promoting future disruptive applications in the era of the Internet of Things, owing to its well-known low training cost and compatibility with hardware. US20150106753A1 US14/198,140 US201414198140A US2015106753A1 US 20150106753 A1 US20150106753 A1 US 20150106753A1 US 201414198140 A US201414198140 A US 201414198140A US 2015106753 A1 US2015106753 A1 US 2015106753A1 Authority US United States Prior art keywords user style alarm styles instructions Prior art date Legal status (The legal status is an 406, Microsoft Corporation, Microsoft Visual C++ 2013 32bit Compilers - DEU Resources.
These remarkable results verify the feasibility of our system and open up a new pathway for the hardware implementation of reservoir computing.Purpose This study examined the time course of contralateral adaptations in maximal isometric strength (MVC), rate of force development (RFD), and rate of electromyographic (EMG) rise (RER) during 4 weeks of unilateral isometric strength training with the non-dominant elbow flexors. Furthermore, it also achieves 97.17 ± 1% accuracy on an actual human motion gesture classification task constructed from a six-axis IMU sensor. Specifically, we numerically and experimentally demonstrate its excellent performance, and our system achieves a high recognition accuracy of 93% on a handwritten digit recognition benchmark and a normalized mean square error of 0.051 in a nonlinear autoregressive moving average task, which reveals its memory capacity. To further simplify and improve the efficiency of reservoir computing, a self-masking process is utilized in our novel reservoir computer.
Labview 2013 Error 1308 Manual Describes Labview
These expanding computing requirements have motivated the creation of new and specialized computing paradigms to break through the “von Neumann bottleneck”. LabViewFundamentals.pdf.Recently, emerging sensor applications, such as the Internet of Things (IoT) 1 and ubiquitous sensing, require sensors with smaller size and lower power consumption, as well as “edge computing” 2 capabilities, to process a deluge of data locally. This manual describes Labview programming concepts, techniques, features, VIs and function you can use to create test and measurement, data acquisition, instrument control, datalogging, measurement analysis and report generation application.
Simple RC based on a time-delayed nonlinear system possessing only a single nonlinear node has been proposed. However, it suffers from the complexity of hardware implementation. Because a mechanism for adaptive changes is not necessary for training, the main two implementation structures are RC based on numerous randomly interacting nonlinear nodes and a time-delayed nonlinear system 10.RC based on spatially extended nodes provides efficient parallel information processing 11, 12. More importantly, the hardware implementation of RC can be achieved using a variety of nonlinear dynamic systems with nonlinearity and fading memory (or short-term memory). RC is different from conventional RNNs in that the weights on the recurrent connections in the reservoir are not trained only the output connection weights in the readout are trained, which makes it possible to drastically reduce the computational cost of learning. As a neuromorphic computing paradigm, reservoir computing (RC) 4, 5, 6 was originally a recurrent neural network (RNN) 7, 8, 9 framework and is therefore suitable for temporal information processing.
Recent studies have aimed to solve this problem by optimizing the system parameters 13, 28, such as mask length and feedback strength, or using different feedback structures, such as double feedback loops 16 and parallel multiple feedback loops 29, 30. However, the delayed feedback and the additional masking procedure reduce processing efficiency. This superiority has recently motivated the search for hardware implementation using emerging devices, such as electronic devices 13, optical systems 14, 15, 16, 17, 18, 19, spintronic devices 20, dynamic memristors 21, 22, 23, 24, 25, and mechanical resonators 26, 27.
Since our mask procedure utilizes the self-nonlinear characteristics of the reservoir, the masking procedure and RC are simultaneously completed, which is why we call it a self-masking process.This allows us to achieve a novel RC architecture using the HNL with the self-masking process for high-efficiency temporal pattern classification, such as the Mixed National Institute of Standards and Technology (MNIST) handwritten digit task, TI-46 spoken word recognition benchmark, and human motion gesture recognition task sensing, from a six-axis inertial measurement unit (IMU) sensor. The self-masking process directly feeds serialized input data into the reservoir, reshaped by the e-exponential characteristics of TNL with a certain temporal solution, and then picks up the nonlinear cumulative response at the separation time. Furthermore, we define a self-masking process to replace the traditional masking procedure to simplify and improve the efficiency of RC. Due to the dynamic richness of the HNL, time-delayed feedback can be removed to achieve high-efficiency RC. Moreover, we focus on the well-known nonlinear dynamics of the micromechanical resonator and first propose a hybrid nonlinear response (HNL), which comprises the transient nonlinear response (TNL) and the Duffing nonlinear response (DuNL). Moreover, an initial report demonstrated the feasibility of RC with a single “delay-coupled” nonlinear microelectromechanical system (MEMS) resonator, and its best classification accuracy was only 78+2% for the TI-46 recognition benchmark.In this work, we propose a novel reservoir computer structure using a single micromechanical resonator with hybrid nonlinear dynamics and omitting time-delayed feedback.
In particular, to obtain a large number of different transient responses to the input, the input signal is time-multiplexed by a mask function that serves the dual purpose of serializing the input and maximizing the effectively used dimensionality of the system. More importantly, the simple structure and device compatibility with MEMS can facilitate the hardware implementation of RC and promote the emergence of disruptive applications using MEMS technology in the future IoT era.Hybrid nonlinear resonator-based RC systemIn the time-delayed RC, structural parameters such as the mask function, the number of virtual nodes, and feedback strength should be optimized to generate a sufficiently rich reservoir state, which reduces processing efficiency. The results show that this novel structure can effectively adjust the nonlinear richness of the system to adapt to the specific pattern classification task 13 it also reduces system multiparameter optimization difficulties and simplifies control loop complexities.
Thus, the reservoir states are sampled through postprocessing, and the training and test procedures are implemented using a linear regression algorithm. Then the self-masking process and nonlinear transformation are simultaneously realized in the hybrid nonlinear reservoir. The serialized input signals are fed to the reservoir after preprocessing. It is composed of three distinct parts: an input layer, a reservoir, and an output layer. The basic principle of our scheme is shown in Fig.
Thus, the masking procedure in time-delayed RC can be replaced with the self-masking process, and the feedback loop is not necessary. We select θ < T ( T = T1 = T3) for better state richness due to the HNL we propose.
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