Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm
نویسندگان
چکیده
منابع مشابه
Color Quantization Using Modified Artificial Fish Swarm Algorithm
Color quantization (CQ) is one of the most important techniques in image compression and processing. Most of quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in ...
متن کاملFuzzy Adaptive Artificial Fish Swarm Algorithm
Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithms which usually employs in optimization problems. There are many parameters to adjust in AFSA like visual and step. Through constant initializing of visual and step parameters, algorithm is only able to do local searching or global searching. In this paper, two new adaptive methods based on fuzzy systems are propose...
متن کاملEmpirical Study of Artificial Fish Swarm Algorithm
Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In stand...
متن کاملAN IMPROVED INTELLIGENT ALGORITHM BASED ON THE GROUP SEARCH ALGORITHM AND THE ARTIFICIAL FISH SWARM ALGORITHM
This article introduces two swarm intelligent algorithms, a group search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group search-artif...
متن کاملA Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters
The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2893765