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Improved fuzzy neural network bus intelligent scheduling method based on chaos theory

A fuzzy neural network and intelligent scheduling technology, applied in the field of intelligent transportation, can solve the problem that the local optimal solution of the particle swarm algorithm is easy to diverge, and achieve the effect of good convergence characteristics

Inactive Publication Date: 2017-01-04
梁广俊
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  • Abstract
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  • Application Information

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Problems solved by technology

But it is precisely for this reason that the basic particle swarm algorithm has the disadvantages of being easily trapped in a local optimal solution and easy to diverge.

Method used

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  • Improved fuzzy neural network bus intelligent scheduling method based on chaos theory
  • Improved fuzzy neural network bus intelligent scheduling method based on chaos theory
  • Improved fuzzy neural network bus intelligent scheduling method based on chaos theory

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Effect test

Embodiment 1

[0071] The system flow of bus intelligent dispatching is as follows: figure 1 shown. Specific steps are as follows:

[0072] The first step is the analysis and investigation of the actual problem demand. Under the relevant assumptions, the problem is described as follows: It is known that there are a total of J stations on a bus route with a total mileage of L. The operating time of the bus company’s vehicles in a day is [t 早 , t 晚 ], and the operating time can be divided into K periods, and the departure interval of the Kth period is Δt k. Assume that the buses for this route are of the same model and arrive at all stops on time. Passengers at each station are uniformly distributed, and the bus fare for each passenger is n. Starting from the operation profit of the bus company and the service level of the bus company (passengers’ waiting time is the shortest), consider the passenger flow and operating conditions of each station in a day, and hope to obtain the best vehi...

Embodiment 2

[0124] On the basis of Embodiment 1, it is further improved to increase the consideration of inertia factors and constraint factors, such as image 3 As shown, in order to keep the particle's motion inertia, it has the tendency to expand the search space and has the ability to explore new regions. However, the inertia weight w of the PSO algorithm with fixed parameters is usually less than 1, and the speed of the particles will become smaller and smaller, or even stop moving, and premature convergence will occur. The shrinkage factor method controls the final convergence of the system behavior, and can effectively search different regions, and the method can obtain high-quality solutions. Combining these two improved strategies together forms a particle swarm optimization algorithm with shrinkage factor and linearly decreasing inertia weight, which not only improves the convergence speed, but also avoids premature convergence.

[0125] Make the following improvements to formu...

Embodiment 3

[0131] Particle swarm optimization algorithm is an algorithm that starts from a group of initial solutions scattered in the solution space, which makes particle swarm optimization algorithm have good global convergence performance. Usually, when the randomly generated initial solutions are scattered in the solution space, the PSO algorithm can converge to the global optimal solution with a high probability. However, if the solution space is very large in practice, the random initial solution may only occupy a small corner, and the fly-operation often stagnates after finding a local optimal solution. This is related to the limited diversity of the particle swarm algorithm when the size of the particle swarm is small, which restricts the ability to jump out of the local optimal solution. Generally, we will increase the diversity by increasing the size of the particle swarm, so as to get rid of the limitation of the local optimal solution. But it often brings huge solution compl...

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Abstract

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, relates to intelligent dispatching of buses by using an improved fuzzy neural network algorithm, and provides a new technical method for vehicle dispatching. Background technique [0002] Urban traffic problem is an important factor that plagues urban development and restricts urban economic construction. In the past ten years, all countries in the world have attached great importance to the increasingly serious traffic problems, and have invested a lot of manpower, material and financial resources in the research on the management and control technology of urban traffic transportation systems. Many different traffic control methods and systems have emerged successively, which have played a huge role in alleviating traffic congestion. In recent years, China's automobile consumption is growing at a rate of 30%, and traffic congestion has become an increasingly serious problem faced by ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/08
CPCG06Q10/04G06N3/086G06Q50/26
Inventor 梁广俊
Owner 梁广俊
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