Optimisation of Linear Passive Suspension System Using MOPSO and Design of Predictive Tool with Artificial Neural Network
نویسندگان
چکیده
منابع مشابه
scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
assessment of the efficiency of s.p.g.c refineries using network dea
data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...
Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models
Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting finan...
متن کاملOptimal integrated passive/active design of the suspension system using iteration on the Lyapunov equations
In this paper, an iterative technique is proposed to solve linear integrated active/passive design problems. The optimality of active and passive parts leads to the nonlinear algebraic Riccati equation due to the active parameters and some associated additional Lyapunov equations due to the passive parameters. Rather than the solution of the nonlinear algebraic Riccati equation, it is proposed ...
متن کاملEVALUATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION MODELS
In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...
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: Studies in Informatics and Control
سال: 2019
ISSN: 1220-1766,1841-429X
DOI: 10.24846/v28i1y201911