Adaptive optimization in the Jalapeño JVM

2000 ◽  
Author(s):  
Matthew Arnold ◽  
Stephen Fink ◽  
David Grove ◽  
Michael Hind ◽  
Peter F. Sweeney
Keyword(s):  
Adaptive Optimization

Distributed Adaptive Optimization with Weight-Balancing

2021 ◽  
pp. 1-1
Author(s):  
Dongdong Yue ◽  
Baldi Simone ◽  
Jinde Cao ◽  
Bart De Schutter
Keyword(s):  
Adaptive Optimization

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator

2021 ◽  
Vol 11 (13) ◽  
pp. 6224
Author(s):  
Qisong Zhou ◽  
Jianzhong Tang ◽  
Yong Nie ◽  
Zheng Chen ◽  
Long Qin
Keyword(s):  
Visual Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Adaptive Optimization ◽  
Tracking Errors ◽  
Kinematics Model ◽  
Compound Control ◽  
Original Intention ◽  
Control Scheme ◽  
Specific Geometry

The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.

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