Description: Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers.
-Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book ( written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers . Platform: |
Size: 4707328 |
Author:王洋 |
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Description: ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的
支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三
个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在
此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估
模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶
产物浓度的实时在线预估。-In view of the hardship to get real—time and on—line biological parameters in fermentation
process，a soft sensor model based on support vector machines is established for estimating the bio—
logical parameters．The complexity and generalization performance of the support vector regression
(SVR)model depend on a good setting of the three parameters ，c， ．An algorithm called parti—
cle swarm optimization(PSO)is applied to optimize the three parameters synchronously．Based on
the proposed method，a PSO—SVR model is developed to estimate the products concentration of beta—
mannanase for feedstuf．The control results of fermenter show that the state estimation model based
on PSO·-SVR has good learn ing accuracy and generalization perform ance SO as to obtain the real·-time
and on—line estimation for products concentration of beta—mannanase． Platform: |
Size: 231424 |
Author:11 |
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Description: Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers. Platform: |
Size: 2941952 |
Author:thomas |
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Description: 机电传感器通常用于获取转子位置/速度的内置式永磁同步电机（ IPMSMs ）的高性能控制
车辆系统。然而，使用这些传感器中的增加的成本，尺寸，重量，布线复杂性并减少了IPMSM驱动系统的机械鲁棒性。这些问题连同
一些实用的要求，例如，调速范围广，极端
环境温度和不利的负载条件下，使传感器控制方案可取。本文提出了一种扩展的反电动势（ EMF ）为基础的滑模
转子位置观测器IPMSMs的无传感器矢量控制。
基于滤波器的特性，一个强大的补偿算法的开发，以改善滑动方式观测器（SMO）的性能。多级滤波器和双滤波器的计划被设计的补偿算法，以进一步改善稳态和瞬态性能，分别。建议SMO和补偿算法通过在MATLAB的Simulink仿真，以及对实验进行验证
实用的永磁同步电动机驱动系统-Electromechanical sensors are commonly used to obtain rotor position/speed for high-performance control of interior permanent magnet synchronous machines (IPMSMs) in
vehicle systems. However, the use of these sensors increases the cost, size, weight, wiring complexity and reduces the mechanical robustness of IPMSM drive systems. These issues, together with
some practical requirements, e.g., wide speed range, extreme
environment temperature, and adverse loading conditions, make a sensorless control scheme desirable. This paper proposes an extended back electromotive force (EMF)-based sliding mode
rotor position observer for sensorless vector control of IPMSMs.
Based on filter characteristics, a robust compensation algorithm is developed to improve the performance of the sliding-mode observer (SMO). Multistage-filter and dual-filter schemes are designed to further improve the steady-state and transient performance, respectively, of the compensation algorithms. The proposed SMO and c Platform: |
Size: 1332224 |
Author:zhangyunlong |
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