This article discusses methods for calculating timecomplexity for combinational and sequential schemes. Also here is the formula for calculating the timecomplexity of SH-model of algorithm.
ISBN:
(纸本)9786176078067
This article discusses methods for calculating timecomplexity for combinational and sequential schemes. Also here is the formula for calculating the timecomplexity of SH-model of algorithm.
This paper presents a novel climate decision support system for tomatoes in high tunnels using fuzzy logic and adaptive neuro-fuzzy inference system. Three climate decision support systems are developed for high tunne...
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This paper presents a novel climate decision support system for tomatoes in high tunnels using fuzzy logic and adaptive neuro-fuzzy inference system. Three climate decision support systems are developed for high tunnels using fuzzy logic. First climate decision support system takes five inputs-temperature, relative humidity, solar radiations, wind velocity, and weather condition-and controls four outputs-tunnel's temperature, tunnel's humidity, fan speed, and shading. Second climate decision support system takes three inputs-temperature, solar radiations, and weather condition-and controls artificial sunlight. Third climate decision support system takes air quality index and controls air purification. We develop and implement the two main algorithms for climate control systems, one algorithm is for fuzzy logic climate decision support system, and other one is for neuro-fuzzy climate control system. We compute timecomplexity of both algorithms. We use software MATLAB for showing average error between calculated and targeted outputs. We also perform optimization of fuzzy membership functions using particle swarm optimization method and evaluate its results in MATLAB. Our generated results are very much precise and satisfied the desired range of outputs.
In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms ca...
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ISBN:
(纸本)9781467385497
In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density-based clustering algorithm, Partitioning clustering algorithm, Graph-based algorithm, Grid-based algorithm, Model-based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes. This paper is planned to learn and relates various data mining clustering algorithms. algorithms which are under exploration as follows: K-Means algorithm, K-Medoids, Distributed K-Means clustering algorithm, Hierarchical clustering algorithm, Grid-based algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.
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