scholarly journals Isotropic Sequence Order Learning

2003 ◽  
Vol 15 (4) ◽  
pp. 831-864 ◽  
Author(s):  
Bernd Porr ◽  
Florentin Wörgötter
Keyword(s):  
Weight Change ◽  
Closed Loop ◽  
Learning Algorithm ◽  
Linear Structure ◽  
Open Loop ◽  
Learning System ◽  
Behavioral Paradigm ◽  
Motor Loop ◽  
Loop Condition ◽  
Spike Time Dependent Plasticity

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, “ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm” (pp. 865–884).

scholarly journals Open Loop and Closed Loop Power Control Analysis on LTE

2018 ◽  
Vol 10 (4) ◽  
pp. 195
Author(s):  
Norma Amalia ◽  
Eka Setia Nugraha ◽  
Muntaqo Alfin Amanaf
Keyword(s):  
Power Control ◽  
Multiple Access ◽  
Closed Loop ◽  
Open Loop ◽  
Control Analysis ◽  
Transmit Power ◽  
Control Scheme ◽  
Loop Condition ◽  
High Level ◽  
Loop Scheme

LTE downlink is using Orthogonal Frequency Division Multiple Access (OFDMA) multiple access system which have high invulnerability from multipath problem. One of the weakness of OFDM system is the high level from Peak to Average Power Ratio (PAPR) that was required higher level transmit power for maintaining the Bit Error Rate (BER) requirement. Using uplink scheme with Single Carrier FDMA (SC-FDMA) which is OFDMA modification, will be offered better level of PAPR than its conventional OFDM. The main problem of using OFDMA is the high level of PAPR, while using SC-FDMA the problem is intra-cell interference. Intra-cell or inter-cell interference is the common problem that can reduce the LTE performance. Minimizing received power for each users (UE) which is still at acceptable tolerance parameter, can be used for reducing the interference problem to another UE. Power control is the appropriate solution for minimizing the interference level. In this paper will be analyzed the power control using open loop and closed loop scheme at LTE network. The simulation result show that without power control schemes, the transmit power of UE is 23 dBm. While, after applying power control scheme, the transmit power is 18.8 dBm at ?=0.4 of open loop condition and 9.05 dBm at closed loop condition. Using this transmit power value as the UE power can improve the SINR performance. The SINR average value without power control scheme is only 20.38 dB which is lower than using open loop scheme is achieved 22.44 dB and 24.02 dB at closed loop scheme.

Baroreflex open-loop gain and arterial pressure compensation in hemorrhagic hypotension

1983 ◽  
Vol 245 (1) ◽  
pp. H54-H59 ◽  
Author(s):  
H. I. Chen ◽  
V. S. Bishop
Keyword(s):  
Arterial Pressure ◽  
Closed Loop ◽  
Control Level ◽  
Systemic Arterial Pressure ◽  
Open Loop ◽  
Loop Gain ◽  
Hemorrhagic Hypotension ◽  
Carotid Baroreceptor ◽  
Loop Condition ◽  
Pressure Compensation

The loop gain (G) of the carotid baroreceptor was determined using open- and closed-loop approach in anesthetized rabbits after aortic denervation and vagotomy. The open-loop relationship between the intrasinus pressure (ISP) and systemic arterial pressure (SAP) was nonlinear. The slope (delta SAP/delta ISP) or G was maximal (about 1.5) near the control arterial pressure and decreased toward the saturation pressures. We examined how this nonlinearity relates to the arterial pressure compensation following hemorrhage. Because the baroreflex attenuation of the posthemorrhagic hypotension depends on the SAP responses to an input perturbation, we first demonstrated that the open-loop responses in SAP to a specific delta ISP was not altered during various amounts of hemorrhage, despite different operating range for the responses. In the open-loop condition when ISP was fixed at the control level, hemorrhage of 10, 20, and 30 ml produced a SAP disturbance (D) of 23.3, 43.5, and 62.4 mmHg, respectively. The figures were minimized to 9.8, 19.8, and 34.4 mmHg (D'), respectively through the baroreflex compensation in the closed-loop condition. The calculated G (D/D' - 1) from the closed-loop data were 1.38, 1.20, and 0.82 for 10, 20, and 30 ml hemorrhage. For a given input signal (delta ISP = D'), the values of G were essentially the same as those obtained from the open-loop ISP-SAP curve. The G values decreased as the degree of hemorrhagic hypotension increased, being in agreement with the sigmoid characteristics of the nonlinear ISP-SAP curve.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Comparison of Dc-Dc SEPIC, CUK and Flyback Converters Based LED Drivers

2020 ◽  
pp. 99-107
Author(s):  
Erdal Sehirli
Keyword(s):  
Integrated Circuit ◽  
Closed Loop ◽  
Input Voltage ◽  
Open Loop ◽  
Current Sensor ◽  
Switching Frequency ◽  
Led Driver ◽  
Advantages And Disadvantages ◽  
Led Drivers ◽  
Conduction Mode

This paper presents the comparison of LED driver topologies that include SEPIC, CUK and FLYBACK DC-DC converters. Both topologies are designed for 8W power and operated in discontinuous conduction mode (DCM) with 88 kHz switching frequency. Furthermore, inductors of SEPIC and CUK converters are wounded as coupled. Applications are realized by using SG3524 integrated circuit for open loop and PIC16F877 microcontroller for closed loop. Besides, ACS712 current sensor used to limit maximum LED current for closed loop applications. Finally, SEPIC, CUK and FLYBACK DC-DC LED drivers are compared with respect to LED current, LED voltage, input voltage and current. Also, advantages and disadvantages of all topologies are concluded.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

scholarly journals Converting MST to TSP Path by Branch Elimination

2020 ◽  
Vol 11 (1) ◽  
pp. 177
Author(s):  
Pasi Fränti ◽  
Teemu Nenonen ◽  
Mingchuan Yuan
Keyword(s):  
Spanning Tree ◽  
Closed Loop ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Open Loop ◽  
Travelling Salesman ◽  
Elimination Algorithm ◽  
Number Of Iterations ◽  
Number Of Branches

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b << n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.

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