A mixed mode real-time VLSI implementation of a shunting inhibition cellular neural network

2002 ◽  
Author(s):  
A. Bermak ◽  
A. Bouzerdoum
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Mixed Mode ◽  
Cellular Neural Network ◽  
Vlsi Implementation ◽  
Shunting Inhibition

Optimal Robot Path Planning with Cellular Neural Network

2013 ◽  
pp. 19-38
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Optimal Robot Path Planning With Cellular Neural Network

2019 ◽  
pp. 491-511
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

REALIZATION OF COUPLINGS IN A POLYNOMIAL TYPE MIXED-MODE CELLULAR NEURAL NETWORK

2003 ◽  
Vol 13 (06) ◽  
pp. 443-452 ◽  
Author(s):  
MIKA LAIHO ◽  
ARI PAASIO ◽  
ASKO KANANEN ◽  
KARI HALONEN
Keyword(s):  
Neural Network ◽  
Mixed Mode ◽  
Cellular Neural Network ◽  
Third Order ◽  
Device Mismatch ◽  
Polynomial Type ◽  
Order Polynomial

In this paper realization of couplings between cells in a polynomial type mixed-mode cellular neural network (CNN) is analyzed. The choice of the multiplier is discussed and two multiplier types are analyzed. Also, two circuits for generating the second and third order polynomial terms of cell output are described. The accuracy of the multipliers and polynomial circuits at the presence of device mismatch is analyzed.

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