The group inverse of subdivision networks

GMDH Type Neural Networks and Their Application to the Identification of the Inverse Kinematics Equations of Robotic Manipulators (RESEARCH NOTE)

ESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...

Evaluation of the role of unilateral and bilateral tooth extraction treatment on maxillary arch symmetry in Class II subdivision cases

Background and Aim: Unilateral extractions have been proposed as potential treatment options to correct Class II subdivision malocclusion, suggesting that most side effects associated with asymmetrical mechanics can be avoided, however the post-treatment arch form may not be symmetrical. Therefore, the aim of this study was to determine the post-treatment effects of unilateral premolar extrac...

REVERSE LOOP SUBDIVISION FOR GEOMETRY AND TEXTURES

Reverse subdivision aims at constructing a coarser representation of an object given by a fine polygon mesh. In this paper, we first derive a mask for reverse Loop subdivision that can be applied to both regular and extraordinary vertices. The mask is parameterized, and thus can also be used in reversing variants of Loop subdivision, such as those proposed by Warren and Litke. We apply this mas...

Using neural networks to predict road roughness

When a vehicle travels on a road, different parts of vehicle vibrate because of road roughness. This paper proposes a method to predict road roughness based on vertical acceleration using neural networks. To this end, first, the suspension system and road roughness are expressed mathematically. Then, the suspension system model will identified using neural networks. The results of this step sho...