Analysis of the Open-Loop Neural Networks

2007 ◽  
pp. 121-141
Keyword(s):  
Neural Networks ◽  
Open Loop

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

scholarly journals Recovery of dynamics and function in spiking neural networks by closed-loop control

2015 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taskin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Brain Activity ◽  
Open Loop ◽  
Spiking Neural Networks ◽  
Delayed Feedback Control ◽  
Loop Control ◽  
Wide Range ◽  
Underlying Network ◽  
And Function

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Tracking Control of a Nonholonomic Mobile Robot Using Neural Network

2015 ◽  
pp. 631-661 ◽  
Author(s):  
Şahin Yildirim ◽  
Sertaç Savaş
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Tracking Error ◽  
Back Propagation ◽  
Open Loop ◽  
Tracking Performance ◽  
Back Propagation Algorithm ◽  
Tracking Errors ◽  
Nonholonomic Mobile Robot ◽  
Feed Forward Neural Networks

The goal of this chapter is to enable a nonholonomic mobile robot to track a specified trajectory with minimum tracking error. Towards that end, an adaptive P controller is designed whose gain parameters are tuned by using two feed-forward neural networks. Back-propagation algorithm is chosen for online learning process and posture-tracking errors are considered as error values for adjusting weights of neural networks. The tracking performance of the controller is illustrated for different trajectories with computer simulation using Matlab/Simulink. In addition, open-loop response of an experimental mobile robot is investigated for these different trajectories. Finally, the performance of the proposed controller is compared to a standard PID controller. The simulation results show that “adaptive P controller using neural networks” has superior tracking performance at adapting large disturbances for the mobile robot.

scholarly journals Dynamic Detection and Reversal of Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

2021 ◽  
Author(s):  
PD Ganzer ◽  
MS Loeian ◽  
SR Roof ◽  
B Teng ◽  
L Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Myocardial Ischemia ◽  
Short Term Memory ◽  
Open Loop ◽  
Myocardial Oxygen Demand ◽  
Bioelectronic Medicine ◽  
Cardiovascular Stress ◽  
Adaptive Architecture ◽  
Reliable Detection ◽  
Biological Neural Networks

AbstractMyocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. Supplementing biological neural networks may enable reliable detection, and potentially even facilitate correction, of myocardial ischemia. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia (preclinical experiments in rats). This responsive bioelectronic medicine uses an ANN with a long short-term memory layer to decode spontaneous myocardial ischemia and autonomously trigger vagus nerve stimulation (VNS) for reducing chronotropy, afterload, and myocardial oxygen demand. We first used injections of cardiovascular stress inducing agents (dobutamine and norepinephrine) that produce a model of spontaneous myocardial ischemia. Next, ANNs were trained to decode spontaneous cardiovascular stress and myocardial ischemia, with an overall accuracy of ~92%. ANN-controlled VNS reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations of stress pathophysiology and unsupervised detection of new emerging stress states. Together, this adaptive architecture provides clinically relevant insights as pathophysiology evolves. Overall, these results provide a first-time demonstration that ANNs can supplement deficient biological neural networks to dynamically detect and help bioelectronically reverse cardiovascular pathophysiology.

scholarly journals Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
M. Funk Drechsler ◽  
T. A. Fiorentin ◽  
H. Göllinger
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Autonomous Driving ◽  
Stability Control ◽  
Open Loop ◽  
Training Process ◽  
Slip Ratio ◽  
Traction Control ◽  
Antilock Braking System ◽  
The Neural Networks

The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.

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