Step1: Assign input weights to neurons Step2: Calculate the learning error for the neural network . . 3. Comparative Analysis The classification performance of IPSONN and HGNN are analysed. HGNN utilised hybrid genetic approach and IPSONN utilized swarm optimization technique.
In other words, the three routes are the same; it's just that the city taken as a departure varies therefore as we are working with 10 cities now there are 10 routes with different starting point. The test was conducted, with a GA, 420 generations were calculated, from reading about this problem I figured this number of generations would help find the ideal route. The program was designed so that the output is written to a text file. 420 generations were written, described and shown in this file. A fragment of the file is shown below: Initial
The initial parameters for the Gaussian mixture model are obtained from the results of the K-means clustering. That is, the initial Gaussian means are equal to the k-means cluster means as mentioned in  and the distribution covariance and mixing proportions are also dependent on the cluster results. μ = μ (10) = (11) Figure 11: Learning representative texture distribution V. EXPERIMENTAL RESULTS In this section, we explains comparison of the proposed TDLS algorithm and Otsu-RGB segmentation algorithm. The Otsu segmentation technique is tested on simple RGB skin lesion image. Figure 12 and 13 shows the results perform based on TDLS and Otsu-RGB segmentation algorithm.
This method can be very effective when dealing with new product or new technologies 2. Time series: Time series forecasting method is done by using historical demand information. The basic concept of the time series method is that the past demand is good indicator for doing forecast of future demand. This method is very effective for the products with steady demand and when the demand pattern does not vary that much from one year to the next year. These are one of the simplest methods to implement in practical field and can provide good starting point to do forecasting (Chopra & Meindl 2007, 186-190) (Emmett & Granville 2007,
Then λ=1/x. b) Process Here we generate random numbers from a particular distribution. From input modeling, if data fits into exponential distribution then here we will generate new data from exponential distribution. This can be done by inverse transform technique. Steps of inverse transform technique: i) We take cumulative function , F(x) ii) F(x)=R iii) X= ------- c) Output analysis The data generated is used for simulation.
We can adopt a simple neural network as our forecasting system and select several technical indicators as input signals. After training the neural network we can test the validity of each individual indicator and its combinations. The experiments are then conducted on the time series data of a major stock index. Based on the results, we can find a more effective trading strategy to improve investment returns. To test the whole model, we should obtain the percentages of accurate predictions for different network topologies, different transfer functions, and different combinations of these basic technical indicators.
The chapter-4 explains the results obtained by carrying out load flow analysis in IEEE-33 bus system using forward backward sweep method & distributed generation allocation as per the local search method. The chapter-5 gives the conclusions that can be made through thesis about DG allocation in three phase radial distribution system. The chapter- 6 gives future scope available in the field of distributed generation allocation. There are three section of appendix A, B and
Changing the traditional transportation scheme to a fully automated and intelligent transportation network is a substantial up gradation of the scheme. The main problems that are hampering this upgrading to materialize are not just technological limits, but cultural, conceptual, social, emotional, political and economical hurdles. In case of managing the large number of vehicles this becomes more complex. III.Literature Survey Freight transport modeling The first freight transport models date from the early 1970s. Over the years, number of dedicated freight transport models have been proposed (e.g., Ben-Akiva et al., 2013; Chow
This can be done using a neural network model based on the theory of conditional probability and Bayes’ rule . A neural network is a computer system modelled on the human brain and nervous system. Queueing theory which is another discipline within the mathematical theory of probability can also be used to calculate the flow of traffic. Calculating traffic flow can be considered as a point process as it consists of single arrivals of discrete entities i.e. individual cars.