Today, the architecture field is witnessing a noticeable evolution regarding the used tools that the designer should invest in a peculiar way that is made available in architecture through the concept of synergy generally and algorithmic synergy specifically. The synergy is meant to study and analyze the cooperative behavior of complex systems and self-organizing systems that leads to different outputs referred to by the synergy as the (whole), which is bigger than the sum of parts and in architecture, it's translated as the architectural form. This point resulted in a need of a specific study regarding the concept of synergy that focuses on the cooperative, synergistic relations within the trilogy of (form, structure, and material) and clarifies the role of technological evolution of design tools through algorithmic synergy in formulating that relation, thus resulted in the research's problem which came in the following statement (The lack of clear knowledge of the algorithmic synergy and its mechanisms in generating and discovering the architectural form digitally) and to solve this problem and Achieving the research goal which is represented in (Clarifying the knowledge regarding the role of algorithmic synergy and its mechanisms in generating and discovering the architectural form digitally), the research clarifies the concept of "Synergy" in general and "Algorithmic Synergy" precisely in order to get the epitome of vocabulary on the theoretical part and moving on to the practical application on elected projects samples moving on to the conclusions and recommendations that shows having the architecture a self-organizing synergy system connects the designer and the developed digital tool that is provided by algorithmic synergy, plays a vital role in reaching the digitally synergized whole that represented by the architectural form.
Abstract
This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per
... Show MoreIn this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method and the least squares method and that using the method of simulation model first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.
The concept of closed quasi principally injective acts over monoids is introduced ,which signifies a generalization for the quasi principally injective as well as for the closed quasi injective acts. Characterization of this concept is intended to show the behavior of a closed quasi principally injective property. At the same time, some properties of closed quasi principally injective acts are examined in terms of their endomorphism monoid. Also, the characterization of a closed self-principally injective monoid is given in terms of its annihilator. The relationship between the following concepts is also studied; closed quasi principally injective acts over monoids, Hopfian, co Hopfian, and directly finite property. Ultimately, based on
... Show More