Decision feedback equalisation using neural network structures and performance comparison with standard architecture

1990 ◽  
Vol 137 (4) ◽  
pp. 221 ◽  
Author(s):  
G.J. Gibson ◽  
S. Siu ◽  
C.F.N Cowan
Keyword(s):  
Neural Network ◽  
Performance Comparison ◽  
Decision Feedback ◽  
Network Structures ◽  
And Performance

scholarly journals 137 Gb/s PAM-4 Transmissions at 850 nm over 40 cm Optical Backplane with 25 G Devices with Improved Neural Network-Based Equalization

2019 ◽  
Vol 9 (23) ◽  
pp. 5095
Author(s):  
Qianwu Zhang ◽  
Yuntong Jiang ◽  
Hai Zhou ◽  
Chuanlu Deng ◽  
Shuaihang Duan ◽  
...  
Keyword(s):  
Neural Network ◽  
Forward Error Correction ◽  
Optical Power ◽  
Pulse Amplitude ◽  
Performance Comparison ◽  
Decision Feedback ◽  
Bit Rate ◽  
Optical Backplane ◽  
The Impact ◽  
Equalization Method

An improved neural network-based equalization method is proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of four level pulse amplitude modulation (PAM-4) signals with 25 G class 850 nm optical devices is achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation is conducted regarding the impact of delayed taps and spans on equalization performance. A performance comparison of the proposed method with the traditional maximum likelihood sequence estimation (MLSE) and decision feedback equalization (DFE) is also undertaken. For the bit rate from 80 to 100 Gb/s, the proposed method achieves an adopted hard-decision forward error correction (HD-FEC) requirement at a received optical power (RoP) of −9 and −8 dBm, while DFE and MLSE cannot meet the HD-FEC requirement. When the bit rate increases from 120 to 137 Gb/s, the proposed equalization method still successfully maintains the acceptable system performance at an RoP of −4 and −2.5 dBm. Furthermore, the specific bit error rate (BER) performances for varied maximum achievable bit rate under different RoPs by applying MLSE and the proposed method are also analyzed. This provides an important potential solution to realize the future data centers.

scholarly journals Guiding Neuroevolution with Structural Objectives

2020 ◽  
Vol 28 (1) ◽  
pp. 115-140 ◽  
Author(s):  
Kai Olav Ellefsen ◽  
Joost Huizinga ◽  
Jim Torresen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Network Structure ◽  
Structural Diversity ◽  
Network Structures ◽  
Improve Performance ◽  
Modular Neural Networks ◽  
And Performance ◽  
High Diversity

The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives—and this technique can even increase performance on problems without any decomposable structure at all.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Design and Performance Comparison of Planar and Linear Antenna Array Using ADS

2014 ◽  
Vol 3 (2) ◽  
pp. 22-26
Author(s):  
V. S. Prabhu ◽  
◽  
V. P. Surya Surendran ◽  
V. G. Veena ◽  
◽  
...  
Keyword(s):  
Antenna Array ◽  
Performance Comparison ◽  
Linear Antenna ◽  
Linear Antenna Array ◽  
And Performance
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