593 research outputs found
Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)
Activated sludge process (ASP) is the most commonly used biological wastewater
treatment system. Mathematical modelling of this process is important for improving its
treatment efficiency and thus the quality of the effluent released into the receiving water
body. This is because the models can help the operator to predict the performance of the
plant in order to take cost-effective and timely remedial actions that would ensure
consistent treatment efficiency and meeting discharge consents. However, due to the
highly complex and non-linear characteristics of this biological system, traditional
mathematical modelling of this treatment process has remained a challenge.
This thesis presents the applications of Artificial Intelligence (AI) techniques for
modelling the ASP. These include the Kohonen Self Organising Map (KSOM),
backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy
inference system (ANFIS). A comparison between these techniques has been made and
the possibility of the hybrids between them was also investigated and tested.
The study demonstrated that AI techniques offer viable, flexible and effective modelling
methodology alternative for the activated sludge system. The KSOM was found to be
an attractive tool for data preparation because it can easily accommodate missing data
and outliers and because of its power in extracting salient features from raw data. As a
consequence of the latter, the KSOM offers an excellent tool for the visualisation of
high dimensional data. In addition, the KSOM was used to develop a software sensor to
predict biological oxygen demand. This soft-sensor represents a significant advance in
real-time BOD operational control by offering a very fast estimation of this important
wastewater parameter when compared to the traditional 5-days bio-essay BOD test
procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to
result much more improved model performance than using the respective modelling
paradigms on their own.Damascus Universit
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey
Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science
Navigation of mobile robot in cluttered environment
Now a day’s mobile robots are widely used in many applications. Navigation of mobile robot is primary issue in robotic research field. The mobile robots to be successful, they must quickly and robustly perform useful tasks in a complex, dynamic, known and unknown surrounding. Navigation plays an important role in all mobile robots activities and tasks. Mobile robots are machines, which navigate around their environment extracting sensory information from the surrounding, and performing actions depend on the information given by the sensors. The main aim of navigation of mobile robot is to give shortest and safest path while avoiding obstacles with the help of suitable navigation technique such as Fuzzy logic. In this, we build up mobile robot then simulation and experiments are carried out in the lab. Comparison between the simulation and experimental results are done and are found to be in good
A Survey of Neural Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of
machine learning, yet coming with mutually exclusive advantages and
limitations. To bring the best of the two worlds, a variety of approaches are
proposed to integrate NNs and DTs explicitly or implicitly. In this survey,
these approaches are organized in a school which we term as neural trees (NTs).
This survey aims to present a comprehensive review of NTs and attempts to
identify how they enhance the model interpretability. We first propose a
thorough taxonomy of NTs that expresses the gradual integration and
co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their
interpretability and performance, and suggest possible solutions to the
remaining challenges. Finally, this survey concludes with a discussion about
other considerations like conditional computation and promising directions
towards this field. A list of papers reviewed in this survey, along with their
corresponding codes, is available at:
https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl
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